{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "20d1844d-56a1-4d97-9098-04cd38614402",
   "metadata": {},
   "source": [
    "# Seasonal lags to capture seasonality"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c7e22b9-366f-4754-ba9c-c642f5b6f291",
   "metadata": {},
   "source": [
    "[Feature Engineering for Time Series Forecasting](https://www.trainindata.com/p/feature-engineering-for-forecasting)\n",
    "\n",
    "In this notebook we will show how we can use seasonal lags to try and capture seasonality. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9a86e7d9-212c-4d82-b896-20a4e078c336",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import re\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_context(\"talk\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e964ccb-f3fd-4e88-a08a-b82e6fc56074",
   "metadata": {},
   "source": [
    "# Data set synopsis\n",
    "\n",
    "The air passengers dataset is the monthly totals of international airline passengers, from 1949 to 1960, in units of 1000s. \n",
    "\n",
    "For instructions on how to download, prepare, and store the dataset, refer to notebook number 5, in the folder \"01-Create-Datasets\" from this repo."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "969cc82e-0ed6-481b-b50a-74d0df94aa52",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9a8af020-f4e4-4bce-8dea-e4e7d57819a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\n",
    "    \"../Datasets/example_air_passengers.csv\",\n",
    "    parse_dates=[\"ds\"],\n",
    "    index_col=[\"ds\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea3cc441-df77-439b-a88d-e74c334635c3",
   "metadata": {},
   "source": [
    "## Plot the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e096e012-faba-477d-b0b3-6d2a2aac6407",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=[10, 5])\n",
    "data.plot(y=\"y\", marker=\".\", figsize=[10, 5], legend=None, ax=ax)\n",
    "ax.set_xlabel(\"Time\")\n",
    "ax.set_ylabel(\"Air passengers (1000s)\")\n",
    "ax.set_title(\"Air passenger numbers\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2683607-c1ab-4b60-b700-05ae7d36f105",
   "metadata": {},
   "source": [
    "# Forecasting with seasonal lag features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80264d9d-247d-4653-a32a-b277b90eba4d",
   "metadata": {},
   "source": [
    "For this problem we know that there is yearly seasonality. So perhaps using a lag feature of 12 months can help us capture the seasonality. See the Lag features section of the course and the notebooks in `07-Lag-Features` for more information around using the various methods to identify potential seasonal lags when we don't know what seasonality to use."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e835157-d0e9-4571-81dc-6fa818ffb032",
   "metadata": {},
   "source": [
    "Let's build a recursive forecast and see how we can include lag features in our feature engineering pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "738606c1-df44-4f9f-b4d7-4e5a1f22d234",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- The transformers from earlier in the course. --- #\n",
    "# Lag and window features\n",
    "from sktime.transformations.series.summarize import WindowSummarizer\n",
    "# Time features for trend \n",
    "from sktime.transformations.series.time_since import TimeSince\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "# Rescaling transformer for linear models with regularisation\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "# Pipelines to create feature engineering pipeline\n",
    "from sklearn.pipeline import make_pipeline, make_union\n",
    "# Used to reset sklearn estimators\n",
    "from sklearn.base import clone\n",
    "\n",
    "# Let's ensure all sklearn transformers output pandas dataframes\n",
    "from sklearn import set_config\n",
    "set_config(transform_output=\"pandas\")  # Upgrade to scikit-learn >= 0.12\n",
    "                                       # for this feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "130d5c18-2870-4a1a-89af-c09bdff6b775",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01-01</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-02-01</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              y\n",
       "ds             \n",
       "1949-01-01  112\n",
       "1949-02-01  118\n",
       "1949-03-01  132\n",
       "1949-04-01  129\n",
       "1949-05-01  121"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data.copy()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d292e0e-e5ce-4c5d-b63a-6490a6457b6b",
   "metadata": {},
   "source": [
    "Specify target name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "25d2262f-c242-409b-93b4-0913f70c3113",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = [\"y\"]  # Note: it's in a list.\n",
    "# This ensures we'll get\n",
    "# a dataframe when using df.loc[:, target]\n",
    "# rather than a pandas Series.\n",
    "# This can also be useful if we have\n",
    "# multiple targets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dac1165-9d17-4648-b7ed-927ed660de3d",
   "metadata": {},
   "source": [
    "Prepare our transformers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "002c3527-412a-4dc4-b359-1c829a8a4789",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Polynomial time features for trend\n",
    "time_feats = make_pipeline(\n",
    "    TimeSince(), PolynomialFeatures(degree=2, include_bias=False)\n",
    ")\n",
    "\n",
    "\n",
    "# Compute lag and window features.\n",
    "lag_window_feats = WindowSummarizer(\n",
    "    lag_feature={\n",
    "        \"lag\": [1, 12],  # Just using the previous lag and a seasonal lag\n",
    "    },\n",
    "    target_cols=target,\n",
    "    truncate=\"bfill\",  # Backfill missing values from lagging and windowing.\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d0525e4-b9a3-4977-88e6-cc213e4d8a9e",
   "metadata": {},
   "source": [
    "Create a pipeline to create all our features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8224a415-81da-4471-9e65-53f0ed4b7ed4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To see how the lag features help\n",
    "# try commenting them out and just using\n",
    "# lag features, then try just using datetime\n",
    "# features.\n",
    "pipeline = make_union(\n",
    "    time_feats,\n",
    "    lag_window_feats,\n",
    ")\n",
    "\n",
    "# Apply min-max scaling to all the features\n",
    "pipeline = make_pipeline(pipeline, MinMaxScaler())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b74e1d09-c547-42dc-b736-f3e55d756749",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;featureunion&#x27;,\n",
       "                 FeatureUnion(transformer_list=[(&#x27;pipeline&#x27;,\n",
       "                                                 Pipeline(steps=[(&#x27;timesince&#x27;,\n",
       "                                                                  TimeSince()),\n",
       "                                                                 (&#x27;polynomialfeatures&#x27;,\n",
       "                                                                  PolynomialFeatures(include_bias=False))])),\n",
       "                                                (&#x27;windowsummarizer&#x27;,\n",
       "                                                 WindowSummarizer(lag_feature={&#x27;lag&#x27;: [1,\n",
       "                                                                                       12]},\n",
       "                                                                  target_cols=[&#x27;y&#x27;],\n",
       "                                                                  truncate=&#x27;bfill&#x27;))])),\n",
       "                (&#x27;minmaxscaler&#x27;, MinMaxScaler())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;featureunion&#x27;,\n",
       "                 FeatureUnion(transformer_list=[(&#x27;pipeline&#x27;,\n",
       "                                                 Pipeline(steps=[(&#x27;timesince&#x27;,\n",
       "                                                                  TimeSince()),\n",
       "                                                                 (&#x27;polynomialfeatures&#x27;,\n",
       "                                                                  PolynomialFeatures(include_bias=False))])),\n",
       "                                                (&#x27;windowsummarizer&#x27;,\n",
       "                                                 WindowSummarizer(lag_feature={&#x27;lag&#x27;: [1,\n",
       "                                                                                       12]},\n",
       "                                                                  target_cols=[&#x27;y&#x27;],\n",
       "                                                                  truncate=&#x27;bfill&#x27;))])),\n",
       "                (&#x27;minmaxscaler&#x27;, MinMaxScaler())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">featureunion: FeatureUnion</label><div class=\"sk-toggleable__content\"><pre>FeatureUnion(transformer_list=[(&#x27;pipeline&#x27;,\n",
       "                                Pipeline(steps=[(&#x27;timesince&#x27;, TimeSince()),\n",
       "                                                (&#x27;polynomialfeatures&#x27;,\n",
       "                                                 PolynomialFeatures(include_bias=False))])),\n",
       "                               (&#x27;windowsummarizer&#x27;,\n",
       "                                WindowSummarizer(lag_feature={&#x27;lag&#x27;: [1, 12]},\n",
       "                                                 target_cols=[&#x27;y&#x27;],\n",
       "                                                 truncate=&#x27;bfill&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>pipeline</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TimeSince</label><div class=\"sk-toggleable__content\"><pre>TimeSince()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures(include_bias=False)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>windowsummarizer</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">WindowSummarizer</label><div class=\"sk-toggleable__content\"><pre>WindowSummarizer(lag_feature={&#x27;lag&#x27;: [1, 12]}, target_cols=[&#x27;y&#x27;],\n",
       "                 truncate=&#x27;bfill&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('featureunion',\n",
       "                 FeatureUnion(transformer_list=[('pipeline',\n",
       "                                                 Pipeline(steps=[('timesince',\n",
       "                                                                  TimeSince()),\n",
       "                                                                 ('polynomialfeatures',\n",
       "                                                                  PolynomialFeatures(include_bias=False))])),\n",
       "                                                ('windowsummarizer',\n",
       "                                                 WindowSummarizer(lag_feature={'lag': [1,\n",
       "                                                                                       12]},\n",
       "                                                                  target_cols=['y'],\n",
       "                                                                  truncate='bfill'))])),\n",
       "                ('minmaxscaler', MinMaxScaler())])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6decf02c-1a0a-45d0-9fec-07255f232a17",
   "metadata": {},
   "source": [
    "Let's check how our feature engineering pipeline behaves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8071a5af-cfb4-4a2b-85d8-d0e065f53fa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_since_1949-01-01 00:00:00</th>\n",
       "      <th>time_since_1949-01-01 00:00:00^2</th>\n",
       "      <th>y_lag_1</th>\n",
       "      <th>y_lag_12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.121212</td>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-02-01</th>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.001736</td>\n",
       "      <td>0.121212</td>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.006944</td>\n",
       "      <td>0.212121</td>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.424242</td>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.378788</td>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-06-01</th>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.043403</td>\n",
       "      <td>0.257576</td>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-07-01</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.469697</td>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-08-01</th>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.085069</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-09-01</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-10-01</th>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.140625</td>\n",
       "      <td>0.484848</td>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-11-01</th>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.173611</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-12-01</th>\n",
       "      <td>0.458333</td>\n",
       "      <td>0.210069</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-01</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.212121</td>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-02-01</th>\n",
       "      <td>0.541667</td>\n",
       "      <td>0.293403</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.318182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-03-01</th>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.340278</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-04-01</th>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.390625</td>\n",
       "      <td>0.560606</td>\n",
       "      <td>0.568182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-05-01</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.469697</td>\n",
       "      <td>0.386364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-06-01</th>\n",
       "      <td>0.708333</td>\n",
       "      <td>0.501736</td>\n",
       "      <td>0.318182</td>\n",
       "      <td>0.704545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-07-01</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-08-01</th>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.626736</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-09-01</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.694444</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-10-01</th>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.765625</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.340909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-11-01</th>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.840278</td>\n",
       "      <td>0.439394</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-12-01</th>\n",
       "      <td>0.958333</td>\n",
       "      <td>0.918403</td>\n",
       "      <td>0.151515</td>\n",
       "      <td>0.318182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-01-01</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            time_since_1949-01-01 00:00:00  time_since_1949-01-01 00:00:00^2  \\\n",
       "ds                                                                             \n",
       "1949-01-01                        0.000000                          0.000000   \n",
       "1949-02-01                        0.041667                          0.001736   \n",
       "1949-03-01                        0.083333                          0.006944   \n",
       "1949-04-01                        0.125000                          0.015625   \n",
       "1949-05-01                        0.166667                          0.027778   \n",
       "1949-06-01                        0.208333                          0.043403   \n",
       "1949-07-01                        0.250000                          0.062500   \n",
       "1949-08-01                        0.291667                          0.085069   \n",
       "1949-09-01                        0.333333                          0.111111   \n",
       "1949-10-01                        0.375000                          0.140625   \n",
       "1949-11-01                        0.416667                          0.173611   \n",
       "1949-12-01                        0.458333                          0.210069   \n",
       "1950-01-01                        0.500000                          0.250000   \n",
       "1950-02-01                        0.541667                          0.293403   \n",
       "1950-03-01                        0.583333                          0.340278   \n",
       "1950-04-01                        0.625000                          0.390625   \n",
       "1950-05-01                        0.666667                          0.444444   \n",
       "1950-06-01                        0.708333                          0.501736   \n",
       "1950-07-01                        0.750000                          0.562500   \n",
       "1950-08-01                        0.791667                          0.626736   \n",
       "1950-09-01                        0.833333                          0.694444   \n",
       "1950-10-01                        0.875000                          0.765625   \n",
       "1950-11-01                        0.916667                          0.840278   \n",
       "1950-12-01                        0.958333                          0.918403   \n",
       "1951-01-01                        1.000000                          1.000000   \n",
       "\n",
       "             y_lag_1  y_lag_12  \n",
       "ds                              \n",
       "1949-01-01  0.121212  0.181818  \n",
       "1949-02-01  0.121212  0.181818  \n",
       "1949-03-01  0.212121  0.181818  \n",
       "1949-04-01  0.424242  0.181818  \n",
       "1949-05-01  0.378788  0.181818  \n",
       "1949-06-01  0.257576  0.181818  \n",
       "1949-07-01  0.469697  0.181818  \n",
       "1949-08-01  0.666667  0.181818  \n",
       "1949-09-01  0.666667  0.181818  \n",
       "1949-10-01  0.484848  0.181818  \n",
       "1949-11-01  0.227273  0.181818  \n",
       "1949-12-01  0.000000  0.181818  \n",
       "1950-01-01  0.212121  0.181818  \n",
       "1950-02-01  0.166667  0.318182  \n",
       "1950-03-01  0.333333  0.636364  \n",
       "1950-04-01  0.560606  0.568182  \n",
       "1950-05-01  0.469697  0.386364  \n",
       "1950-06-01  0.318182  0.704545  \n",
       "1950-07-01  0.681818  1.000000  \n",
       "1950-08-01  1.000000  1.000000  \n",
       "1950-09-01  1.000000  0.727273  \n",
       "1950-10-01  0.818182  0.340909  \n",
       "1950-11-01  0.439394  0.000000  \n",
       "1950-12-01  0.151515  0.318182  \n",
       "1951-01-01  0.545455  0.250000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit_transform(df.head(25))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faf8a062-c2be-4d22-9b1f-73be7f52b4f0",
   "metadata": {},
   "source": [
    "Let's reset our feature engineering pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "22ad95c0-b937-471c-a2ae-fe17ed2d3f2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can use `clone` to return an unfitted version\n",
    "# of the pipeline.\n",
    "pipeline = clone(pipeline)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9445dbf-0c67-4e52-9df7-ffc5ff178ef6",
   "metadata": {},
   "source": [
    "Let's build a recursive forecast."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c79eb0e-d6e1-40eb-a930-80900822f66f",
   "metadata": {},
   "source": [
    "We'll start with configuring the model, the forecast start time, the number of steps to forecast, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3e812426-089d-4add-bbb7-8a630e879efd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from lightgbm import LGBMRegressor\n",
    "from sklearn.linear_model import Lasso, LinearRegression, Ridge\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fee3871f-b8ca-4063-9272-37c0f71f626a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- CONFIG --- #\n",
    "# Define time of first forecast, this determines our train / test split.\n",
    "forecast_start_time = df.index.max() - pd.DateOffset(months=36)  # Start 3 years from the end. \n",
    "\n",
    "# Define number of steps to forecast.\n",
    "num_of_forecast_steps = 12 * 3  # 3 years into the future\n",
    "\n",
    "# Define the model.\n",
    "model = LinearRegression()\n",
    "\n",
    "# Create a list of periods that we'll forecast over.\n",
    "forecast_horizon = pd.date_range(\n",
    "    forecast_start_time, periods=num_of_forecast_steps, freq=\"MS\"\n",
    ")\n",
    "\n",
    "# How much data in the past is needed to create our features\n",
    "look_back_window_size = pd.DateOffset(months=12)  # We need the latest 12 time periods\n",
    "                                                  # in our predict dataframe to build our\n",
    "                                                  # lag features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ffaef60-a54d-4d0c-a19b-e4e5202ec896",
   "metadata": {},
   "source": [
    "Let's create our training dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e25af278-6269-4c5f-8c8c-7ff0cd1c01fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- CREATE TRAINING & TESTING DATAFRAME  --- #\n",
    "# Ensure we only have training data up to the start\n",
    "# of the forecast.\n",
    "df_train = df.loc[df.index < forecast_start_time].copy()\n",
    "df_test = df.loc[df.index >= forecast_start_time].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d2997ce-4793-4d66-abc5-9127486d8ad0",
   "metadata": {},
   "source": [
    "Let's compute our `X_train` and `y_train` and fit our model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "528ab868-ea31-40cf-aafb-410e29fe9eb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --- FEATURE ENGINEERING--- #\n",
    "# Create X_train and y_train\n",
    "y_train = df_train[target]\n",
    "X_train = pipeline.fit_transform(df_train)\n",
    "\n",
    "# LightGBM cannot handle column names which have\n",
    "# certain characters (e.g., \":\"). We replace these\n",
    "# with `_`.\n",
    "if \"lightgbm\" in model.__module__:  # checks if model is from lightgbm\n",
    "    X_train = X_train.rename(columns=lambda x: re.sub(\"[^A-Za-z0-9_]+\", \"_\", x))\n",
    "\n",
    "\n",
    "# --- MODEL TRAINING---#\n",
    "# Train one-step ahead forecast model\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bb9e82a-2228-40a7-a73b-9b7a31391cf0",
   "metadata": {},
   "source": [
    "Let's prepare the dataframe that we will pass to `model.predict()`. This will contain some portion of time series during the training period so we can create any features that require historic data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d80416ff-ee17-4e88-a715-86f2a49fb40e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- CREATE DYNAMIC PREDICTION DATAFRAME  --- #\n",
    "# We will recursively append our forecasts to this\n",
    "# dataframe and re-compute our lag and window features from the\n",
    "# target in this dataframe. It contains data in both the training period\n",
    "# and forecast period which is needed for some transformers (e.g., lags and windows).\n",
    "look_back_start_time = forecast_start_time - look_back_window_size\n",
    "\n",
    "# Create `df_predict` which has data going as far back\n",
    "# as needed to create features which need past values.\n",
    "df_predict = df_train.loc[look_back_start_time:].copy()\n",
    "\n",
    "# Extend index into forecast horizon\n",
    "df_predict = pd.concat([df_predict, pd.DataFrame(index=forecast_horizon)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65945f51-985d-46e3-a137-21cd2965312e",
   "metadata": {},
   "source": [
    "Let's recursively create `X_test` and make our predictions and append them to the `df_predict` dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d629a47f-6f84-4dea-8a53-4e7c4d1fae9c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# --- RECURSIVE FORECASTING LOOP --- #\n",
    "for forecast_time in forecast_horizon:\n",
    "    # Compute features during the forecast horizon\n",
    "    X_test_ = pipeline.transform(df_predict)\n",
    "    X_test = X_test_.loc[[forecast_time]]\n",
    "\n",
    "    # Predict one step ahead.\n",
    "    y_pred = model.predict(X_test)\n",
    "\n",
    "    # Append forecast to the target variable columnn in our\n",
    "    # dynamic forecast dataframe `df_predict`. This `df_predict`\n",
    "    # is ready for the next iteration where we will re-compute\n",
    "    # features derived from the target such as lags and windows.\n",
    "    df_predict.loc[[forecast_time], target] = y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45b746cd-135f-46b4-8929-4b7e9ecd391d",
   "metadata": {},
   "source": [
    "Let's retrieve our forecast and actuals during the forecast horizon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7931aa06-0468-41dc-b8a0-43dca249500d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- GET FORECAST AND TEST VALUES --- #\n",
    "y_forecast = df_predict.loc[forecast_horizon, target]\n",
    "y_test = df_test.loc[forecast_start_time:, target]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8c65617-d15a-48ad-b326-faa79c51b2f8",
   "metadata": {},
   "source": [
    "Let's create predictions on the training set using our one step ahead forecast model. This is useful to plot when debugging models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3b511c05-ce4e-449d-b849-2ab556d681d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- CREATE IN-SAMPLE PREDICTIONS--- #\n",
    "y_forecast_train = model.predict(X_train)\n",
    "y_forecast_train = pd.DataFrame(y_forecast_train, index=X_train.index, columns=target)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86ba2671-7108-4a0d-a696-ba88fc844cf1",
   "metadata": {},
   "source": [
    "Let's plot the forecast!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ca0a3741-e4ec-4811-9e64-ce682b83a30f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Recursive forecast with LinearRegression()')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PHcR0Rtqvl93s98abvewjxKBIxl2IEaK1XquU+jumXnyO1votTGu5t4H/WvXkH2P+s5mIyUY9ggmcwARWhwL3K6UOt45TmNaBLkzWD0x9/S+AJUqpuzDlF9+n/3//pwOXKqWWYDKQfsxH3UcAT+qO9oG9eRj4OaZ1ZQiTdeuqP89Bf12F+ej9OaXUn6zHMRf4IWZyXU9lEF3dZZ3nVqXUoZggtgHTDu8wTE3ydwC01k8ppZ7APH9TlFL/xHxaMhXz3NmT9J7G1I//Ryn1LKZDzY+IvgLtV0qp9zCfdmzDZBLPx7y2kW1A3wPOUUpdB3yFec6f12YdgaisWuBPMG0SN2qtN0Tc/AbmufLT0d6xN19iMqAXKaVaMJnZSq11PCa1RpqslLq6h9vu6O3x9kN/3pdPY8qNnsCUkORjWiBWE183YiZKLwYO01p/oMy6DIuAlUqpp+h4f+yNKU/xAGitv1BK3Yt537xq/V3nYjpAfWztH+snJgsxrRKfVEo9iXnftWO6yhyNyZSfiZkcu936G/gYcyFYhnlua+m4mLhPKTUeUza2CfNJzw+t47stftRFvP7Go3kKOEYpNbvLJ4W2o4HPtNbSClIMneFuYyNf8vVt+qKj5d3lPdw+HZOdeyNiWw6m1GE1JgCsw9RT3wXs2uX4DEzHj7WY/yirMQHVD7rsdzTmI2If5j/ymzEfh2uit4M8M8pY98T8p7cW0y2iAfgEc1HgjeUc1u2fWbe/0svzFvNz0MPxi4jSDtK6rQyTmay0nrP1mAAoKdZzWLe7MBchH1jPRzOmVOGvwOFd9nVgAqIVmFrmRkx3oWsi9nFiek2vtV6nTdZrOz3K63QFJgiptPbdjAkq9upyv3mYUpoaTNDe4+Ppcpzds/6BLtvPs7Yv6+W9fmaX7Udbj7vNun1pDO+1h7CSsTGMVffxVRCx30NRjn0oyjnPtG47ZCDvS8wnULdar2Gb9b64AnNR1+kx93RfEbcvBZp6efx/s46fF7HtGExnmJqI98d/gAu7HOsErsF0qvFZ78kfYMpvNBHtGPt6TazH/Gvr+WjFvMe/wvSl38/ax4O5OF2O+bfKhynZ+QswJeJcCzBdcLZY+1RhstgnxfI3Shz+xonSShXzqWI1Udo9YjoQhYCfxfK+lS/5GuiX0vqbUoIohBBCiMFSSj2P+TQvTUcsmCZAKXUF5gK7TEdMAldK3YFZWXWq7rmtpxCDJjXuQgghxLeQ6lhrIHLb7piuNK9L0B7VnZiynsvtDUqpQkwb14UStIuhJhl3IYQQ4ltIKXUhZu7FvzDlKNMwNe8O4CCt9ce9HC6EGAESuAshhBDfQlanqesw81eyMHXpbwOLtdYfjeDQhBA9kMBdCCGEEEKIMUDaQfZBKRXAfGzYMNJjEUIIIYQQ32hpQEhrHTVGl4x7H5RSIUClp6eP9FCEEEIIIcQ3WH19PZjWq1EbyEjGvW8N6enp6XV1dSM9DiGEEEII8Q2WkZFBfX19j1Ue0g5SCCGEEEKIMUACdyGEEEIIIcYACdyFEEIIIYQYAyRwF0IIIYQQYgyQwF0IIYQQQogxQLrKxElzczMNDQ0EAgFCodBID0eIMcXhcOByuUhLSyM5OXmkhyOEEEKMShK4D1IoFGLbtm00NjbicDhwu904nc6RHpYQY4rf76elpYW6ujpSU1MpKirC4ZAPBIUQQohIErgPUn19PY2NjeTk5JCdnS3BhhADFAqFqK6uZufOndTX15OZmTnSQxJCCCFGFYkyB6mpqQmPx0NOTo4E7UIMgsPhICcnB4/HQ1NT00gPRwghhBh1JNIcpFAohMvlQik10kMRYsxTSuFyuWSeiBBCCBGFBO5CCCGEEEKMARK4CyGEEEIIMQZI4C6EEEIIIcQYIIG7GDFKKRYtWjTSwxBCCCGEGDCtNSE9PHOzJHAXvXrvvfdYtGgRdXV1Iz0UIYQQQohRJRgKcsaLZ3DskmNp9jcP+f1J4C569d5777F48eIhCdxbW1u5+uqr435eIYQQQojhsLZuLR9Xfszmxs18tOOjIb8/CdxFXASDQXw+X7+OSUhIwOWSNcCEEEIIMTZ9tvOz8M9ratcM+f1J4C56tGjRIi699FIAysrKUEqhlGLjxo0opbjkkkt45JFHmDZtGl6vl3fffReA2267jQMPPJDs7GwSExPZe++9efrpp7udv2uN+6JFi1BKsWHDBk4//XTS09NJT0/nrLPOoqWlZVgesxBCCCFErD6t+jT885q6oQ/cJd05ylU2trG5ppXirETyUhOG9b4XLFjAunXreOyxx7jjjjvIyckBIDc3F4CXX36ZJ554gp/97GdkZGRQWFgIwF133cXxxx/Pj3/8Y9rb2/n73//OySefzAsvvMAxxxzT5/2edNJJTJo0iZtuuokVK1Zw//33k5eXx8033zx0D1YIIYQQop86Be7DkHGXwH2IBIIhtte3DeocL35ewW0vr8LlUARCmssP34UjdysY0LkK0xNwOfv3Acvuu+/O3nvvzWOPPcYJJ5xAaWlpp9tXr17Nl19+ydSpU7ttT0xMDP9+8cUXs9dee3H77bfHFLjvu+++3HPPPeHfq6ureeCBByRwF0IIIcSo0djeyPr69eHf19evxx/y43a4h+w+JXAfItvr25hzyxtxOZddOX7Dv7/ihn9/NaBzvPW/36E4Kyku47Edeuih3YJ2oFPQXltbSzAYZM6cOfztb3+L6bwXXnhhp9/nzJnDkiVLaGhoIC0tbXCDFkIIIYSIg893fo5Gh38PhAJsqt/E5MzJQ3afUuMuBqysrCzq9hdeeIH999+fhIQEsrKyyM3N5c9//jP19fUxnbekpKTT75mZmYC5CBBCCCGEGA3sMpnJGZNJcacAQ1/nLhn3IVKYnsBb//udAR9f3eTjh/e+hy/Q0dDf63LwxPn7k53iHdB44i0ys2576623OP7445k7dy5/+tOfKCwsxO128+CDD/L444/HdF6n0xl1u9Y66nYhhBBCiOFmd5TZI3cPUtwprKxayZraNRxVdtSQ3acE7kPE5XQMqjSlOCuJ3y6YyVVLPsPtcOAPhbjxxJnsWZIZx1H2TSnVr/2feeYZEhISeOmll/B6Oy4wHnzwwXgPTQghhBBiRGitwxn33XN3x6Ec4cB9KEngPoot2Gs8B0/JGbGuMgDJyckAMS/A5HQ6UUoRDAbD2zZu3Mhzzz03BKMTQgghhBh+Wxq3UOszJby75+yOL2hmJA51qYzUuI9yeakJ7D0hc0SCdoC9994bgIULF/Loo4/y97//nebmnpf0PeaYY2hpaeHII4/k7rvv5tprr2W//fZj8uShm6ghhBBCCDGcPt1psu0p7hQmZkxkSsYUALY2baXZ33OcNFgSuItezZo1ixtvvJFPPvmEM888k1NPPZWqqqoe9z/00EN54IEHqKio4JJLLuFvf/sbN998MyeeeOIwjloIIYQQYujYZTIzcmbgUA6mZE4J3zaU5TJKJvz1TilVl56ent5TqcimTZsAmDBhwjCOSohvLvmbEkIIMdqd+sKpfF79OefNPI+f7/VzAA576jAqWyr5zQG/4eSpJw/ovBkZGdTX19drrTOi3S4ZdyGEEEIIIWLkC/r4uvZrwHSUsdlZ96HMuEvgLoQQQgghRIy+qv6KQCgAwMzcmeHtUzPMopQSuAshhBBCCDEK2PXt41PGk5WQFd4ezrjXrRmytWckcBdCCCGEECJGdkeZ3XN377TdDtzrffVUtfbcyGMwJHAXQgghhBAiRp9VmRVTuwbuE9Mn4lRm9fehKpeRwF0IIYQQQogYVLVUsa15G2AWXorkcXqYkGY6okngLoQQQgghxAiyy2Q8Dg/TsqZ1uz2yzn0oSOAuhBBCCCFEDOwymenZ03E73d1ut1dQlYy7EEIIIYQQI+jL6i8BmJkzM+rtUzNNS8h1devCLSPjSQJ3IYQQQgghYrC9eTsAxanFUW+3S2XaQ+2UN5bH/f4lcBdCCCGEEKIPWmt2tOwAID8pP+o+RSlFJLmSgKEpl5HAXQghhBBCiD40+ZtoDbQCkJ8cPXB3KAeTMycDEriLEfDee++xaNEi6urqhuT8FRUVLFq0iJUrVw7J+YUQQggh4qGypTL8c15SXo/7DeUEVQncRa/ee+89Fi9ePKSB++LFiyVwF0IIIcSoZpfJOJWT7ITsHvez69zX1a+L+xgkcBdCCCGEEKIPdsY925uBs725x/2KkosA2NG8A611XMcggbvo0aJFi7j00ksBKCsrQymFUoqNGzcC8OCDD7LXXnuRmJhITk4OZ5xxBjt27Oh0jg8//JAjjjiCnJwcEhMTKSsr4+yzzwZg6dKlzJo1C4CzzjorfP6HHnpo2B6jEEIIIUQsdmz7CICClnp49nwoXx51v9ykXADagm00+ZviOgZXXM8m4q+tAVqqISkbEtKG9a4XLFjAunXreOyxx7jjjjvIyckBIDc3l8WLF3Pttddy6qmncv7557N9+3buuusuPvjgAz766CMSExOprKzk8MMPp6ysjKuvvprk5GQ2bNjAkiVLAJg+fTo33HADCxcu5Pzzz2fOnDkAHHjggcP6OIUQQgghetXWQOWm/wKQFwxBSw0suxPy7u4Wn+Uk5oR/rmqtItWTGrdhSOA+VEIhaKsb3Dm2roD37wYdBOWE/S6EcXsN7FwJGeDo3wcsu+++O3vvvTePPfYYJ5xwAqWlpQBs3LiR6667jltuuYVf/OIX4f2POuooDjzwQB5++GEuvPBC3nnnHWpra1m1ahW5ubnh/W688UYA8vPzOfroo1m4cCEHHHAAp5122sAemxBCCCHEUGqpplK3A5CnFfjqwJtikqtdAvfsxGwUCo2murWaiekT4zYMCdyHSlsdvLZ44McH22HDW+B0g8MFoQC8cAmUzQGnp//nO+waSMoa+HgiLFmyBK01CxYsYOfOneHtkydPprCwkKVLl3LhhReSkZER3v/cc8/F0c8LByGEEEKIUSEpmx2YlVDztYKQ32Tdk7pPUnU73GQmZFLTVkNVS1VchyGR1GgV8AHaBO1gfdfW9pG1Zs0aQqEQEydOJDc3t9PXtm3bqKoyb9J58+Zx0kknccEFF5Cbm8vJJ5/Mo48+Snt7+wg/AiGEEEKIfkhIY4fLDUCevx2CfkgvBqWi7m6Xy1S1xjdwl4z7UEnIMFnugfI1wr9+AZ5kcCeCvxXam805vQOolUrIGPhYugiFQjidTv7zn/+gorxhMzMzAVBK8fTTT/P+++/z/PPP89JLL3H66adz2223sWzZMlJSUuI2JiGEEEKIoeIP+qnxNwKQnzMdEvJNUnXDf2HaMd32z03MZXXtana27ux222BI4D5UHI7BlaYkZcHc/zUTH/xt4HCa3zMnxG2IsYgWmE+aNIlgMMiUKVPCde+92W+//dhvv/24/vrrefLJJ/nhD3/IE088wTnnnBP1/EIIIYQQo0lla8TiS95MmPgd2PiWKWuedKhJskbITjQlNPHOuEupzGhWMhtOvBuOusl8L5k97ENITk4G6LQA04knnojD4eDaa6/ttn8oFKKmpgaA2trabv1L99xzTwDa2tp6PL8QQgghxGjSadVURwJMPQKcXgi0wsa3u+2fm2iacuxskYz7t0tC2rC3gYy09957A7Bw4UJOOeUU3G43xx13HNdeey1XX30169at47jjjiM5OZl169bxzDPPsHDhQs4991wefvhh/vSnP3HCCScwadIkmpubuf/++0lLS+Poo48GoLS0lKysLO6++25SU1NJTk5mv/32o6ysbMQesxBCCCFEJHvV1DTlIjEh3ZQtlx4E616H1S9DxgRILQjHbHYv929cqYxSal9gEXAg4AbWAXdorR+K2Od4a59dgUrgAeAGrXWgy7kygFuAE4Ek4H3gMq31yqF9FN9cs2bN4sYbb+SPf/wjL774IqFQiA0bNrBw4UKmTJnCnXfeyTXXXIPD4aCkpIQTTjiB7373u4CZnLp8+XKeeOIJduzYQXp6OrNnz+bRRx8NB+Yul4tHHnmEX/3qV1x44YUEAgEefPBBCdyFEEIIMWpUNpuMe57DC14roTrxEPj0Kdi+EratgJR8OOgSKJn9zZycqpQ6CvgHsBT4NeAHpgLFXfZ5Dngd+B9gJvAbIMf63d7PAfzLuv02oBq4CFiqlNpba71uyB/QN9SVV17JlVde2W37D37wA37wgx/0eNysWbN4/PHH+zz/McccwzHHdJ/YIYQQQggxGtgZ93yHFzx2cw0F9ZtN6+5AG3iSwosy2aUyDe0N+II+vE5vXMYxYoG7UiodeAj4s9b6//Wy623Ax8ARWuugdWwDcKVS6vda6zXWft/HZO1P1Fo/Z+33JLAauAY4fSgehxBCCCGE+Gaza9zzHd6O7n4t1ZCYBf4Ws/6O1hAKQkt1OHAHUy4zLmVcXMYxkpNTfwRkYLLnKKVSVZcWI0qpXTHlMffYQbvlT5ixnxSx7fvANkwGHwCtdRXwJHCCUso9BI9BCCGEEEJ8w9mBe57ymhVTwSy+5E6AUMj87ms0XQCTssNdZYC4LsI0koH7fOBr4Gil1GagAahRSt2klHJa+8yyvn8YeaDWehuwJeJ2e9+PdNc2JrAcSAUmx3n8QgghhBDiW8AulcmLzLgnpMFBlwIhk3X3NZoa94Q0ktxJJLtN57x4TlAdyRr3yZha9ocwE0o/Bo4FfgUkAJcAhda+26Mcvx0oivi9EFMHH20/rH2/6nqjUqquj3Gm93G7EEIIIYT4htJady6V8UQshFkyG2YsgNoNMOOkTq27cxNzafY3x3WC6khm3FOATOA3Wutfa62f1VqfDTwFXKSUygHsbva+KMe3RdyO9XNP+9FlXyGEEEIIMcQCoQB3fnQnS9YsGemhDFitrxZ/yA90qXG3JWVZ2zoXfdidZb4pGfdW6/vfumz/K3AyMDtin2hTcRMibrfP19N+dNk3TGud0dsgrYy8ZN2FEEIIIfrplU2v8MDnD+BSLo4sO5JE19jLo+5o3hH+2ZTKpHTewWNKYmhv7rQ5vAhTHAP3kcy42yUsO7pst3/PjNinkO4KMZNRI8/X03502VcIIYQQQgyxlza+BEBAByhvKB/h0QyMXSbjwUGGcnfPuNvtIdubOm3OSbJ6uX9DJqd+ZH3v2h9nvPW9Clhp/bxP5A5KqSJrv5URm1cCe3ftTAPsBzQBawc1WiGEEEIIEbNmfzNvbXkr/Ht549gM3DsmpnpQSnWucYdvTcb9Kev7OfYGK+g+F2gG3tNaf4HpPHN+RKcZgJ8CIeCZiG1PYyagfi/ifDmYspt/aK39Q/EghBBCCCFEd0s3L6U91B7+fVPDppEbzCCEW0E6vOBKBGeXSvMeAvehWD11xGrctdYfKaUewSyklAesAI4BjgD+V2vdYO36S+CfwEtKqSeA3YCLMb3dV0ec8mngPeARpdRtwE7MyqkOYNEwPCQhhBBCCGGxy2RsG+s3jsxABqnTqqldy2QgInDvUipjBe41bTUEQ0GcDmfXI/ttJDPuAOcBN2CC9bswLSIv1Frfau+gtX4BWABkA3+wfr4e+HnkiawFmo7GLLj0c+BWTLnNd7TWUiYjhBBCCDFMmtqbWLZ1GQAT0iYAY7dUpqMVZEL3iakQUePebFZPtdilMiEdotZXG5exjGjgrrVut1pBlmitPVrraVrre6Ls95zWepbWOkFrXay1vkZrHYiyX63W+lytdY7WOllr/R2t9YrheTRCCCGEEAJg6RZTJuNxeDht+mnAN6RUpreMO9osxGTJTcoN/xyvCaojnXEXY8D777/P/vvvT1JSEkopNm7cONJDEkIIIcQoZpfJHDzuYGZkzwBMyUi9r34khzUgdjvIvK6LL9nCgTud6tzTPGl4HB4gfnXuEriLXvn9fk4++WSampq46667ePTRR8nNze37wDGooqKCRYsWsXLlypEeihBCCDFmNbY3hstkjig9gpK0kvBtY60lZIu/hUZ/IwD5qqeMe0T5TESdu1Iq7oswjeQCTGIMWLduHZs3b+ahhx7ijDPOGOnhDKmKigoWL15MaWkpe+6550gPRwghhBiTlm5eij/kx+PwMK94HsnuZDK9mdT6atnUuImZuTNHeogxs8tkoJfJqU43OL0Q9HXvLJOUw7bmbVIqI4ZHZaV5w6anx2fx2JaWlr53EkIIIcSY9fLGlwGYM34OyW5TRmJPUB1rde524K6AHIcn+uRU6LklZEJ8W0JK4C56dOaZZzJv3jwATjzxRJRSHHLIIQC89tprHHTQQSQlJZGZmcnJJ5/Mhg0buh2fkZHBmjVrOPLII0lJSeGiiy4CIBQKcdtttzF9+nS8Xi+FhYX8z//8D01NnVspATz88MPss88+JCUlkZWVxWGHHcZbb3Us6PDggw9y6KGHkpeXh9frZdddd+XPf/5zt/N8+OGHHHHEEeTk5JCYmEhZWRlnn302AEuXLmXWrFkAnHXWWSilUErx0EMPDfp5FEIIIb4tGtobWLato0zGZpfLbKofW4G73QoyS3lwK0f0jDv02BLSnqAqpTKjXCAUCL/Yo0F+Uj4uR/9e7gsuuIBx48Zx4403cskll7D33nuTn5/Pq6++ylFHHcXUqVO57rrraGho4K677uKggw7ik08+6VQD7/f7OeKIIzj00EO5/fbbyczMBOCcc87h8ccf5+yzz+aSSy5hzZo1/N///R9ffvklr776KvYCuL/+9a+5/vrrmTNnDtdffz1KKd59913eeust5syZA8Cf//xnZsyYwfHHH4/L5eL555/noosuIhQK8bOf/QwwnxwcfvjhlJWVcfXVV5OcnMyGDRtYsmQJANOnT+eGG25g4cKFnH/++eFzH3jggYN74oUQQohvEbtMxuv0Mm/8vPD20rRSADY2bByRcQ1Ux6qpXrMh2uRU6NwSMoLUuI8RO1p2cOQzR470MMJePOlFxqWM69cxBxxwAD6fjxtvvJF58+ZxwgknADBr1ixycnJYtmwZGRkZABx11FEccMAB3HTTTfzud78Ln6OlpYXTTjuNa6+9Nrztrbfe4qGHHuLpp5/mpJNOCm/fd999OeWUU3jppZc48sgjWbNmDTfeeCMnn3wyf//733E4zAdEl156KTqiT+qbb75JYmJi+PeLL76YI488kttvvz0cuL/zzjvU1tayatWqThcWN954IwD5+fkcffTRLFy4kAMOOIDTTjutX8+VEEIIITq6ycwdP5ckd1J4e2Qvd611OEE32nX0cLcC9z4z7p0Dd7uXe7wCdymVEf2yfft2Vq5cydlnnx0O2gH2339/9t9/f/71r391O+bCCy/s9PvTTz9NVlYW8+bNY+fOneGvuXPn4nQ6Wbp0KQDPPfccoVCI3/zmN+Gg3Rb5Bx8ZtNfX17Nz507mzZvH+vXrqa83bafssS5ZsoRQKDSYp0AIIYQQUWit+bDiQwAOLTm002124N7sb6a6rXrYxzZQdivIfIcXHC5weaPv2FPgbpXKVLVUdUo6DpRk3IdIflI+L5704kgPIyw/KT8u59m0ydSm7bLLLt1umzZtGn//+987bfN6vRQVFXXatmbNGmpqanpsK1lVZSZwrF+/HqfTybRp03od07Jly7jmmmt49913u01+ra+vJz09nXnz5nHSSSdxwQUXcOWVV3LooYdy/PHH88Mf/hCPx9P7gxZCCCFEn+p99bQEzP/Dk9IndbotsiXkxvqN4RKS0a7b4ks9fVIQLpXpXONuP872UDsN7Q2kewfX7EMC9yHicrj6XZryTZSQkNBtWygUorCwkEceeSTqMV0D/d6sW7eOww47jGnTpnH77bdTXFyMx+Ph3//+N3fccUc4u66U4umnn+b999/n+eef56WXXuL000/ntttuY9myZaSk9DBLXAghhBAx2dq8NfxzUUrn/8sTXYnkJ+Wzo2UH5Y3l7FOwz3APb0A6lcp4eokVeuoqE3GBsrN1pwTuYnhNmGA+6lq1alW321atWhW+vTeTJk3ijTfeYM6cOXi9PXzkZO0XDAb5+uuv2W233aLu8/zzz+Pz+fjnP/9JSUnH1fwbb7wRdf/99tuP/fbbj+uvv54nn3ySH/7whzzxxBOcc845Y6beTgghhBiNtjdtByDJlUSaJ63b7aVppexo2TFmJqgGQgF2tpnadJNx7/6YwnoI3LMSslAoNJqq1iomZUyKcnDspMZd9EthYSF77rknDz74YLh+HGD58uW8++67HHPMMX2e4/vf/z7t7e3cdNNN3W7z+Xw0NDQA8L3vfQ+Hw8HixYu71aXbdWJOp7PT72DKYx588MFO+9fW1narLbMXWWprawMgOdn80dXV1fX5GIQQQgjR2bambYDJtkdLho21lpA7W3cS0ib+6HHxJZudjfe3QETM4nK4yErICp9vsCTjLvrt1ltv5cgjj+Sggw7i7LPPDreDLCws5Iorrujz+O985zuce+65LFq0iI8++ojDDjsMh8PB6tWrefLJJ/nrX//K/PnzmTJlCr/61a/47W9/yyGHHMIJJ5yAy+Xi3XffZebMmVx11VUcfvjheDwejjvuOC644AKampq47777yMvLY/v27eH7fPjhh/nTn/7ECSecwKRJk2hubub+++8nLS2No48+GoDS0lKysrK4++67SU1NJTk5mf3224+ysrIhey6FEEKIb4rtzeb/3cLkwqi3j7VFmCJXTTUZ9xhKZQD8zZ2C/NykXKrbqtnZIoG7GAHz58/nxRdf5De/+Q0LFy7E6/Uyf/58br311h4nnHZ17733svfee3PvvfdyxRVX4PV6KSsr47zzzgtnwsG0aywrK+OPf/wjV111FcnJycyaNSu8MNQuu+zC008/zdVXX83ll19OQUEBP/3pT8nNzQ0vrgQwb948li9fzhNPPMGOHTtIT09n9uzZPProo+HA3OVy8cgjj/CrX/2KCy+8kEAgwIMPPiiBuxBCCBGDyIx7NHYv982NmwmGgjgdzuEa2oDYgXuycpGiXH1k3CMC9/bOgbtd5x6P1VMlcBe9OuSQQ6K2L5o/fz7z58/v9djeVh1VSnHhhRd2axUZzXnnncd5553X4+3HHXccxx13XLftZ511VvjnWbNm8fjjj/d5X8ccc0xM5T5CCCGE6Gxbcw+Be1sDtFQzISEbMB1WKloqRn0Tj47Fl6xGGz0tvgTdA/cIdi93CdyFEEIIIcSoEM64J0cE7uXLYdkdEAoxTjlwKgdBHWJT/aZRH7iHH4/DahvdW8bd4QRXIgRae2wJGY8ad5mcKoQQQgghBqXZ30xDu2kuUZhi1bi3NcCbN0H1OmirxZ2QwjhtcsZjobOMHbgX2hn33mrcoc+WkFUtg8+4S+AuhBBCCCEGxQ5yISLj3lwF9eWAhtZacHqZoEz2uryxfARG2T/h0p9w4N5Lxh36XD1VMu5CCCGEEGLE2R1l3A432YnZ1saV4G+DUMD87mtkgjLrt4yFjLvdl95k3BW4k3s/oIfVU+0a9yZ/E62B1kGNSQJ3IYQQQggxKFubzKqpRSlFOJQDajfCxrehYCYE/aa/eVs9EyYfCYz+Xu4t/hZqfbWAlXH3JIOjj7A5xtVTB0MmpwohhBBCiEEJZ6eTCyHgg48fAx2Cwj0hKdsE7vtfxASHH1b/lW3N2/AH/bid7pEdeA8qmivCPxc5EnpfNdXWR6kMmMC9OLV4wOOSjLsQQgghhBiUcD14Qg58cD80bAWHC2adBomZ4fpwexGmkA6xuWnziI23L/bjcSkHuX0tvmTroVTG6/SSarWSHOwEVQnchRBCCCHEoIQz7uvfgvf+BBvegqyJkD6uY1Knr5GC5AI8VnvF0VwuY0+2zXen4VSq74mp0GPGHeK3CJME7kIIIYQQYlC22TXuvjZwJ5ks+8a3TUvIiIDWoRyUpJUAsKlh9AfuRS5r7J5YMu49B+72BNXB1rhL4C6EEEIIIQbMF/Sxs60agKKQtdp61iQIhaClultAW5pWCsCmxlEcuDd37eHejxr3QBsEA51uilcvdwnchRBCCCHEgNllMgBFgaD5QQfNaqJJ2d1qv+3JmVsatwzrOPvDfkxFyl41tR817tCtzj0vKQ+QjLsYQosWLUIpNdLDGHFLly5FKcXSpUvjds5Vq1Zx2GGHkZaWFvdzCyGEEMPJzk47cZAXCJgOMsF2OOgSSEjrFrjbQWw8VhIdKuHJttoKlftT4w49r546yBp3aQcpxAg444wz2LJlCzfddBNpaWlMnz59pIc0ZNra2rjppps45JBDOOSQQ0Z6OEIIIeLMzk7nJWTiKpsOIQ1H3wwJ6WaHLqUydnvEypbKYR9rLPxBf/iiokhZ7SpjqXF3JwEK0N1bQkqNuxhqV199Na2tg1vhS3TX2trK+++/z3nnncdFF13EaaedRn5+/kgPa8i0tbWxePFi+VRBCCG+ocL14N5McHogY3xH0A7dAvf8JPN/XqO/kRZ/y7CONRYVzRVoTK1+UbjGPYaMu8NhBe90Xz3VulipaavBH/QPeGwSuIseuVwuEhISRnoY3zhVVeYqPj09vY89Y9fc3H0GuxBCCDEcwvXgLiu4Tejy/1u4VKYZQqFwqQwMvnRkKNgXIgpFgXaArzH2g3tahCmxYxGmamsi70BI4C56FK3GXSnFJZdcwjPPPMOMGTPwer3MmDGDF198MaZzVlRUcNZZZzF+/Hi8Xi+FhYV873vfY+PGjeF9HnzwQQ499FDy8vLwer3suuuu/PnPf+52rtLSUk444QReeeUV9tprLxITE9lrr71Yvnw5AA8//DBTp04lISGBefPmsWHDhk7HH3LIIey555588MEHHHDAASQmJjJ58mQeeuihmB7LO++8w3e/+13S0tJITk5m/vz5fPTRR70es2jRIiZMMItPXHrppSilKC0tDd++YsUKjjjiCFJTU0lNTeXII4/kk08+6XYOpRSrVq3ihz/8Ienp6Rx77LGdnj/7+cjJyeGMM85gx44d3cbyr3/9i7lz55KSkkJ6ejoHHnggzz33XPj2f/zjHxxzzDEUFRXh9XqZNGkS1113HcFgsNN51qxZw0knnURBQQEJCQmMHz+eU045hfr6ejZu3EhmZiYAixcvRimFUopFixbF8hQLIYQYA7barSCdVra5aweWcO23Bn9LpyB2NJbL2K0gc13JuDcugy0fwj8uhvLlfR8cw+qpg6ntlxp30W9vvvkmTz31FBdddBEpKSn8/ve/56STTqK8vJzs7Oxejz3ppJNYv349F110EUVFRVRUVPDKK69QXl4eDmD//Oc/M2PGDI4//nhcLhfPP/88F110EaFQiJ/97Gedzvf1119z+umn89Of/pTTTjuNm266iWOPPZbf/va33HLLLVxwwQU0NDRw8803c+655/Laa691Or66uppjjjmGH/3oR5x66qn87W9/46yzziIpKYkf/OAHPT6OV199laOPPpr999+fa6+9llAoxL333svcuXP54IMP2HXXXaMet2DBAjIyMrj00ks57bTTOOKII0hJMZmIL774grlz55KZmcmVV14Zfi7mzJnD+++/360OfsGCBUyfPp2bb74Zt9vU4C1evJhrr72WU089lfPPP5/t27dz11138cEHH/DRRx+RmJgIwAMPPMC5557LHnvswcKFC0lNTeWjjz7ilVde4YQTTgDgoYceIiUlhcsuu4yUlBRef/11fvOb39DQ0MCtt94KQHt7O0cccQROp5PLLruM7OxsNm/ezAsvvEBdXR25ubncc889XHDBBZx44oksWLAAgN13373X94kQQoixY3tzlw4sCV0Cd2/nbitubwpZCVnUtNWwo6V7YmmkhR+P3wdOt/nEwJsMy+6EvLu7P75I4cC9c6lMsjuZRFcirYHWwX3KoLWWr16+gLr09HTdk40bN+qNGzd22x7y+7Vv85ZR8xXy+3t8DD255pprtHmLdAC01+vV69evD2/75JNPNKD/8Ic/9Hq+2tpaDegnn3yy1/1aWlq6bTviiCP0xIkTO22bMGGCBvT7778f3vbEE09oQGdmZuqdO3eGt1955ZUa0OXl5eFt8+bN04D+/e9/H97W2tqqp0+frsvKynQoFNJaa/3GG29oQL/xxhtaa62DwaCePHmyPvbYYzuNp66uThcUFOgf/vCHvT6+DRs2aEDfcccdnbafcMIJOiEhodP7acOGDdrr9eoFCxaEt9mvy09+8pNu53U6nfq2227rtP3dd9/VSin95z//OTzOlJQUfeCBB+q2trZO+9qPWevor8MFF1ygk5KSwsd9/PHHGtDLly/v8fHar/s111zT4z6RevqbEkIIMfr4g369x8N76N0e2k0v+8//0/qfP9d67Wuddwr4zfZ//lzrnWu11lp//5/f17s9tJv+y2d/Gf5B92HhWwv1bg/tpn/5wF5a/2Efre87zIz9sR9oXb2+94M/ftzs++FD3W46+pmj9W4P7ab//tXfezw8PT1dA3W6h7hUMu5DxF+xg3Xz54/0MMImvfoqnvHj4nKuI444grKysvDvu+++O2lpaaxfv77X4xITE/F4PLz44oscffTRJCcn97ifrb6+Hr/fz7x583jppZeor6/vVBu+++67M3v27PDv++23HwDHH398p+y/vX3Dhg0UFxeHt3u9Xs4999zw7wkJCZxzzjlcfvnlrFq1imnTpnUb3yeffMLatWtZvHgxO3d2nh0+Z86cAU3CDAaDvPzyyyxYsCBcSgOmHOjEE0/k+eefJxgM4nQ6w7ddeOGFnc6xZMkStNYsWLCg07gmT55MYWEhS5cu5cILL+Tll1+mqamJK6+8Eq/X2+kckaVRka9DY2MjPp+POXPmcM899/D111+zxx57hF+L559/nj322AOPx9Pvxy6EEN9mWms+qfqEsvQy0r3xm/s0XCpbKglqU0JZFLQWX+pa4+50gSsRAq2dWkJ+XfP1qCyVCWfcQwpCAXB5TYtLuy99b3pbPTUpl/LG8kFl3KXGXfRbSUlJt22ZmZnU1tYCpnyioqKi01cwGMTr9XLzzTfz0EMPkZOTw2GHHcYdd9wRnqxpW7ZsGfPnzyc5OZmMjAxyc3O56qqrABPI9zYWO5CMDM4jt9tjtI0bN65TgAowZcoUgE5195HWrFkDwI9//GNyc3M7fT311FPdHk8sqqqqaGlpYZdddul227Rp02hubu52kRB58WSPKxQKMXHixG7j2rZtW3hc9gXWbrvt1uuYvvjiC0488UTS09NJS0sjNzeX0047Deh4HcrKyrjsssu47rrryM7O5thjj+Wee+6hsbEfE3mEEOJb7IHPH+An//kJC99eONJDGRC7Hhyg0J4DFe0CpGtLSKvOfTSWyoRr9jPKIOiH9hbwNXf0pe9Nb4F7HFpCSsZ9iLgL8pn06qsjPYwwd0H82g1GZn0jaVNaxDvvvMN3vvOdTrdt2LCB0tJSLrnkEo4//niee+45Xn75Za644gquv/56Xn/9dfbYYw/WrVvHYYcdxrRp07j99tspLi7G4/Hw73//mzvuuINQKBTTWPoa42DYY7jjjjv6DH6HUtcLjlAohNPp5D//+U/UhbPsSaKxqKurY968eaSlpXHttdcyadIkEhISWLFiBb/61a86vQ6/+93vOOuss/jHP/7BSy+9xEUXXcQNN9zAu+++y7hx8fmURwghvonW1a3jTyv/BMCnVZ+O8GgGxu7AkpOQhVdZ//d2zbiDCWhbdoLPZNztlpCjbRGmYCjIjmZzMVHozYSyOTD5CJh8aN9BO3RbbCqSvQjTYD5lkMB9iCiXK26lKWPNHnvswSuvvNJpW0FBQfjniRMnctlll3HZZZexfv169thjD2677TYeffRRnn/+eXw+H//85z87ZdPfeOONIRnr1q1baW1t7RQE2xn1yJKVSJMmTQIgIyOD+XEqh8rNzSUpKYlVq1Z1u23VqlUkJyeTk5PT6zkmTZpEMBhkypQpnTrVRNsP4PPPP+9xv6VLl1JdXc2zzz7L3Llzw9u7duax7bbbbuy2224sXLiQ999/n/3335+7776b6667TlbfFUKIKIKhIL955zf4Q6and62vlmZ/M8nu6GWko5WdcS9KiPg/KlqAa/dB77J66mgrlalqrSKgAwAUBUPgTIT8XWML2qFzxl1riPg/0O4sM5iMu5TKiLjLzMxk/vz5nb4SEhJoaWnptqBTaWkp6enptLW1AR2Z8sjMeH19PQ8++OCQjNXn83H//fd3+v2BBx6gtLQ0an07wF577cXEiRO57bbbaGnpvnDEQEplnE4nhx9+OM8++yzl5eXh7eXl5SxZsiTcuaU3J554Ig6Hg2uvvbbbbaFQiJqaGgC++93vkpKSwo033ojP5+u0n/28R3sd2tvb+dOf/tRp/4aGBgKBQKdtu+22Gy6XK/yaJiYmopSirq6u1/ELIcS3yeNfP94ty76lccsIjWbg7HrwQq/1qa4rwdSEd9WlhCQcuLdWEtKh7vuPEPvxABTaiy/1VdceyX6cIT8E2zvdZJfKDKbGXTLuYtisXr2aww47jJNPPpldd90Vj8fDc889x9atWznllFMAOPzww/F4PBx33HFccMEFNDU1cd9995GXl8f27dv7uIf+GzduHNdddx3r1q1j0qRJPP7443z11Vc8/vjjPWaKnU4n9957L8cccwwzZ87kjDPOoLCwkC1btvDyyy8zefJkHn300X6P5frrr+eVV17h4IMP5qc//Slg2kG6XC6uv/76Po+fPHky1157LVdffTXr1q3juOOOIzk5mXXr1vHMM8+wcOFCzj33XNLT0/nd737HBRdcwH777ccpp5xCWloaK1aswOv18sc//pEDDzyQzMxMzjjjDH7+85+jlOLRRx/tVmr0+uuvc/HFF/P973+fXXbZhWAwyGOPPYZSipNOOgkAj8fDzJkzeeKJJ5g6dSpZWVnhDL0QQnwbbW7czB8+/gMAJ005iX9v+DetgVa2NG1hl6zuc51Gs3DG3Z0K/lD3Hu62HgL3QChAbVst2Yn9CI6HkP14Mj1pJCknoKKX/vTEE9n6srnTRUzk6qmBUACXo/9huATuYtgUFxdz6qmn8tprr/HYY4/hcrmYNm0aTz75ZDjI22WXXXj66ae5+uqrufzyyykoKOCnP/0pubm5nH322XEfU05ODvfddx//8z//w9133824ceN44IEHOPXUU3s97rDDDuOdd97h2muv5a677qK5uZmioiIOPPDAbt1eYjVjxgz++9//csUVV3DDDTcAcNBBB3HzzTd36+Hek4ULFzJlyhTuvPNOrrnmGhwOByUlJZxwwgl897vfDe93/vnnk5eXx80338y1116Lx+NhxowZ/OpXvwIgOzubF154gV/84hdcffXVZGZmctppp3HYYYdxxBFHhM+zxx57cMQRR/DCCy9wzz33kJSUxB577MF//vMf9t9///B+9957LxdffDGXXXYZPp+Pa665RgJ3IcS3ktaaxe8spjXQSl5SHr/Y5xd8UvUJa+vWsrVx60gPr9/CGXdnEtDUc5Dbpb9519VTR0vg3u0ThIQ00xUnVp6IUqf2JkjKCv9qZ9xDOkRNW02n5yBWKh6T9b7JlFJ16enp6T19zL9p0yag53poMXodcsgh1NXVsXLlypEeioggf1NCiG+yZ1Y/w6J3FwHwf4f+H/OK53Hxaxfz5pY3OXXaqVy131UjO8B+COkQ+zy2D/6Qn/8r+z7z6mtg3D6w10+671z+PnzyOCRmwvxFaK3Z+7G98Yf8/PGwPzJ3/Nzux4yAxe8u5unVTzM/azfuIA8yy+DgS2I/gdbwr8tAh2C/CyGvI/HW0N7AQX87CIC/H/t3ZmTP6HZ4RkYG9fX19VrrjGinlxp3IYQQQohh0OJv4Xcf/g6Ao8uOZl7xPADGp44HOtoQjhXVrdXhybVF2u4oE1upjFIqnHEeTS0htzdZGXeH1bQiMfaObICZjNpDS8hUdypepymd2dkysAmqErgLIYQQQgyDdXXraPSbdS4u3+fy8PbxKSZwH2uTU+1WkBCx+FJfNe7BdgiYSZt24D6aWkLaj2mccpsNEaUuMeuhJaRSKtwScqATVCVwF0IIIYQYBpsaTSlghjcjPFERYFyKaR+9tWlrXNYbGS52djrNk0Zyu9U1rsca98hJm6OzJaTWuiPjbje6SRxI4N73IkwDvViRyaniW2vp0qUjPQQhhBDfIuUNpt1vSVrnVb/tUhlf0MfO1p2dgvrRzM5OFyUXgt8KUvuanArhSZujrVSm1ldLW9C0Mi4KBMHJADPuvQTuSYNrCSkZdyGEEEKIYbCpwZp8n9p58r2dcQfY0jR2ymXs1omFiREXGj0F7u5EUFbYabeETBxdGXc72w5QqDzmh/7WuIP5dCHYDrUboa2h002D7eUugbsQQgghxDDY3LgZ6J5xT3InkZVgMrtjqc7dbp1Y5M3o2NhTjXuUSZujrcbdnhyc7EokTVlFKQMJ3Bu2woa34LOnYcmFUL48fFN49VSZnDoyHA4HgUBgTNWkCTFaaa0JBAI4HPJPkxDim8fOuJekdg7caWtgfIKZtDgmM+5uK1h3J4HL0/MBnlTzvUuNe62vlvYuq4yOhPCFSEK2WYTRkxJ9FdjetDXAqv+A020+ZfAmw7I7w5l3e3JqZevAPmUYsf8dlVKHKKV0D1/Tuux7oFLqbaVUi1KqQil1l1IqKco5vUqpm5VS25RSrUqp95RShw3l40hJSaG9vZ2qqipCodGzZK8QY00oFKKqqor29nZSUlL6PkAIIcaQurY6GtpN8DYhLaJUpnw5LLmAcfUVAGyt+GQkhtdvWuuOQNdptU7sKdtu6yHjDqOjXKZjFVjrcQwk295SbT5dcLgg6ANXEoSCZjsd5UE1rTWEdP/jxtEwOfVO4KMu28L9hZRSewKvAV8AlwHjgcuBicBxXY57CDjJOuda4EzgP0qpeVrrd+M9cID09HRaWlqorq6mtrYWt9uN0+kcirsS4hsrGAzi9/sJhUKkpaWRnt6P5aWFEGIMsDvKQESpTFsDvHE9NGxlfJrJR27ZsdJs76kf+ijR0N5AszUhtciuB+9rzHbg7uu+emplS2V4ku5IsSfbFjqTIMTAAvekbPPJQ8i6EGmrB4fTbAdykkzGPaAD1LbV9nvF2NEQuL+ptX6ul9tvBKqBQ7TWTQBKqY3AfUqpQ7XWr1vbZgOnAJdqre+0tj0CfA7cDAzJklwOh4Nx48aRkZFBQ0MDgUBAMu9C9JPb7SYxMZH09HSSkrp9mCaEEGOe3VEmKyGLVLtkpHkn1G8Bh5Px7T7wwBbtM9nZUR64VzRXhH8uwF58qY+ki7dzqUyCK4E0TxoN7Q0DLh2JJ3tyatFgergnpMHcX8KzF5gJqq3V8J1fh1/P3IiJvDtbd47JwB2lVCrQqrUOdNmeBnwXuNUO2i2PAHcAPwBet7Z9H/AD99s7aa3blFIPADcopQq11tsZIsnJySQnJ/e9oxBCCCG+dcobrVaQkfXtNetMcIeb8UETklUSoN2bSi+V4qOCXVaS4Ewgy2+Fb/0slQGTdW9ob6CyeeQD93DGPWRVkg+khzvAhANhxglmkupuJ0PJ7PBNGd4MXA4XgVCAypZKdsnapV+nHg0zwB4FGoBWpdTLSqmZEbfNxFxcfBh5gNa6HVgJzIrYPAv4ukuAD7AcUMCe0e5cKVXX2xcgn9kLIYQQYlDCE1PtMpmADza8CQUzIehnnK8FAA1sC3QNZUYfO8gtSC5A+ayWh31l3HsI3GHka9y3N22nsd2salsUsC5EBpJxt2WUmE8YrHPalFLhrPvO1v53lhnJwL0deBr4f8D3gMXAbOBtpdRUa59C63u0TPl2oCji98Je9qPLvkIIIYQQwya8+JKdcV+/FHyNkJIHZXPIL5qNS5mSE7st4WgWLitJKYJ+B+4dFybhwH0ES2VCOsSvl/0agExvBpMdCeaGgdS421ILzPfGim43DaaX+4iVymit3wHeidj0T6XU85js+jXAjwFrmjK+KKdoi7gd6+ee9qPLvpHjyOhtnJJ1F0IIIcRgaK3DgfuEtAlm8una18yN4/eFLR/gcnooUHlsad4+Jnq52x1lChPzoNYO3PsqlbFr3JtBa1BqVGTcH/niEd6veB+ARbtfTNI6KzwdaKkMQIoVuDd1XxXWbgk5kP71o6FUJkxr/QnwKmC3cGy1vkdropkQcbu9b0/70WVfIYQQQohhUeurpdFvSiZK0kpgzcumVaA7GaYfH95vvNUqcExk3O3APSEiuPXGmHFHg9+UBo306qlfVX/FXR/fBcD3p36fQzOsmnNXgunDPlCp+eZ7e5P5ZCWCvQjTQDLuoypwt2wG7HeBXeZSGGW/QiLaRlr79rQfXfYVQgghhBgWdrYdoMQfhNX/MZNSpx5ustROk3ccZ6+eOgYWYeroeZ7asTHWdpDQrSVkZUvlsC9m2Rpo5Yq3riAQClCaVsov9/kltNaYGxMzTT/2gUrJ7/i5qfNFyWBKZUZj4D4RsB/J50AA2CdyB6WUBzPZdGXE5pXANKVU15Vb9rO+j40VDYQQQgjxjWJ3lMlyp5Gy5AIofw/K3w8H7HZAO95jaqpHe6mML+ijus0sKFTotFr4upPNaqG98USEaPbqqcl54XPaC1QNl999+DvW16/HpVzcNPcmktxJ0GIH7oMokwGz4qp9ji517nbGfWfLGJqcqpTKjbLtYOA7wEsAWut6TOnMT7oE5D8BUoCnIrY9DbiBcyPO5wXOApZprSXjLoQQQohhZ3eUmeD3g7/VLNCTVQbv/MHUu3tM8DveZUKd0Z5xj+zhXuiwLj5i6TvvdJkSFAgH7vlJHZnp4SyXeXPzmzyx6gkALp51MTOyZ5gbWmvN98F0lLGFJ6h27p0SrnFvrer3pwwj2cf9CaVUC2aC6k5gN+B86+dFEfsttPZZqpS6H7Ny6i+A/2itX7V30lq/r5R6CrhFKVUIrAPOACZgVlAVQgghhBh24Y4y2gEOK/RKGweNO8xiS1YmerzTZN4b2xup99WT3lfN+Aixy2QcykFeyCon6auHu82TDIG2cEvITG8mTuUkqINUtlQyJXPKUAy5k2AoyM0f3AzAvgX7cuaMMztutAP3wXSUsaXkQeWX3Sao2qUy/pCfel89GQkZMZ9yJEtlngNyMUH4H4GTgMeBfbXW4WIwrfUKYD6mY8wdwHnAfcDJUc55OnCX9f33mAz80VrrZUP2KIQQQgghehHOuGsHhALg9ECgFRxOSMo2GXhgvKOjx8ZonqBqT0zNS8rDbfdk76sVpK1LL3enwxnOQA9Xxv2trW+xuXEzAFfvdzVOh7PjxngG7qnWNMuugXtSR9FJf+vcRyxw11r/Xmu9n9Y6W2vt1lqP01qfHRm0R+z7ttb6IK11otY6X2v9c611c5T92rTWv9RaF2qtE7TWsyOz8kIIIYQQw0lrHa5xL87fC4J+8+VrhoMuMSUmVjCbHgyS7DY/j+Y69/DE1OQiaKs3G2MO3O2WkB293O1ymeEK3B/76jEADio6iIkZEztu8LeGu93EpVTGnqDaVg/tLeHNWQlZOK2e/WMmcBdCCCGE+Karbqum2W9yjRNSx0PZHJh1Gpx4N5TMNjtZpTIq0Mr4lPHA2Mi4FyQXRCy+1I9SGRix1VNX167m/e2mZ/uPp/+48432xFQY/ORU6NJZpiPr7lAOshOygf73cpfAXQghhBBiiNglGQAlgZApk8mb3jnQtSan0t7MuJRxwOjOuNuBe1HKADLuXqvXiK8j426XjgxH4P74V48DUJpWykHjDup8o10m43CBN5VB8yR11P53KZfJSeqYoNofErgLIYQQQgwRu749JzGHZDvITcruvFNEFnp8qsm4j+bOMnapTGFirploCv2bnAqdSmXCGffWoQ3ca9tqeWH9C4DJtjtUlzA4Xj3cI4U7y3RuCWkvPLWztX8tISVwF0IIIYQYIuGOMinjO4Lc5JzOO9n9zSMy7qO1VCYYCrKj2WSPi9wRWfaYa9w7HqttuGrcn179NL6gj1R3KsdPOr77DuGJqXEok7HZ5TI9ZdylVEYIIYQQYnSwM+4lCRHBelLXwN1eUVQz3moVuLVpK8FQcBhG2D87W3cS0AEgYvElGEDGvSNwt0tlqlur8Yf8cRlnV/6Qn79//XcATpp6kllsqSu7xj0eE1NtdmeZroswDXD1VAnchRBCCCGGiN1RZoInw2xwJ3fUtNsigsjxHhM0BkKBfgd1w8GubwcodHoh2A6hYEc3lr7YgXvQZ7rr0FEqo9FUt1bHdby2Vza+QmVrJQ7l4NRpp0bfqXE7+Bo7FomKBzvj3loDAV94c3gRJsm4CyGEEEKMPK11R6mMM9FsTM7uvqOnY3H4ce6OnyMnto4Wdn17hjeDpG2fwIa3oPxdWHIhlC/v+wSeiEmfw7h66l+/+isAhxYfaibVdlW+HD59Eja/D+/fE9tjiUVq9M4y9sXKztad/Vo9VQJ3IYQQQoghUN1WTUvAZKInaGvF1K5lMgAuDzjcAHhDgfDExdFY576t2ZqYmpRvAl2n29SEe5Nh2Z3Q1tD7CcJlQYTLZZLdyeH+9UMRuH9a9Smf7vwUgNN2Pa37Dm0N8PbvzIRUd5Kp14/lscTCm9pxYdbYEbjbpTJtwTaa/E3RjoxKAnchhBBCjFqVjW18tKmWysa2kR5Kv9n17QDFwZD5oevEVFtkS8jU0dsScnuTKZUp9GaaybYOF7i8JuANBaGlj1IXdyLY3Vx83Rdh2tGyI9pRg7J081IApmROYa+8vbrv0FINDdvNY0GZGvdYHkuswhNUO+rc7VIZ6F+5jATuQgghhBg1IgP1Z1dsYe4tb3DmX5Yz95Y3eHbF6Atke2OXyeQm5pLUareC7Clw7+i2UphsJjRWNFdE33cEhXu4pxZDMAChgKkJ97eAw9m91WVXSkWdoGo/ZrsUJ57W1K4BYM/cPVHR2jy21pivUAAyiiHkj+2xxCpKS8jsxGwUZiz9mcvgis+IhBBCCCEG59kVW7hqyWe4HQ58gSDBEAS1pg2Trb5qyWccPCWHvNQ4Th4cQuGOMqnFHSuM9phx7whmC5JNoDeaA/fC9FLInQbbPzYBuNMDB10S2wqqnhQzCbS9MbzJfsyRk1/jZU2dCdynZE7pfmN7C3z5DyiYCdXrQDnB1xz7Y4lFlJaQLoeL03c9nWRPcviiJRYSuAshhBBixFU2tnHVs5/RFgiFA/Wu3A4Hm2tax0zgHu4ok5gHje1mY09Z3IjA3Q7khiKIHQytdTgjXuRJg+QsKJsDe58NubvEHuh6kk03mpr1ULQXJKQN2acMzf7m8FyBKRlRAvfPnjKrv6YXw+E3mE8EkrLjF7RDR8a9eafppOM08xku3/fyfp9KAnchhBBCjLjNNa0oQhy/7m2KmnfiCfpxhwJ4gn4CDhd/22U+O90FFGcljvRQY2Z3hSn2pANVZgJqTwsVWZMz8TdTmDUJMEGs1jp6eccIaGhvCE+2LcRjNjq9ULRnOBiNSVOl6UZT8TmseRUOuoTClKG5WLHLZKBLxr2tATa8CZvfM58WzDgBsifG9b7DUvLNhUrAB9XrIW+XAZ9KAnchhBBCjLjirERmblvFTz97LurtGii65eYxk22HjiB0nMMac3KOyehGE6VUpj3UTk1bDdmJcaq1HqROPdy1NU0yMbN/QXtbA2z4rznGkxTuRlN40PmAaY/YHmzH4/TEZcx2mUxeUh7pXuuiqXw5/PcW2LkadAh2/R6UHBCX+4uqahVsfAd0AP51KRy2CEpmD+hUMjlVCCGEECMuLzWB2aGdADR4knireBZ1cw/Ht8sMAEobKzh6Zuy1wCOtxd9Cvc9MSC0MWeFWb5MdI7rK2IE7jK46d7tMJsGZQGa7teBSSl7/TtJSberIHS6Tgba60RQ6vOFddjTHr7OMnXEPZ9vbGkyrx9Za0w3HlWgCeF9jzycZjLYGWHaXKb1xJwFqUK0mJXAXQgghxKiQUW0yuvW7zuKEZ//CAffeRfpZZwEwrqmKrTXNvR0+qkQG3IXBoPmhp4mpENFVpok0TxpJLhPIj6Y69/DE1JRClN0qMTm3fydJygZ3gungEmgzk0MdTvIzp4a7rNi94uPBDtynZkw1G1qqrZVeW83v2RMBR/xaP3Zl35/XXnhKD6rVpATuQgghhBhxTb4AmVbgnrfr1HBJTP5u0wBICPqpWL1hxMbXX3aQ61IucnxWdrqnVpAQUSrTgoJR2RLSzrgXJheaOnXof+CekAb7/9RM0vQ1momhB12COzk7vChRvC5WtNbdO8okZQMhCFiBu3LFt/VjV0nZ5vz26qjtTYO6PwnchRBCCDHiVm1voLjRBIP5M6aGtyeWTiDgcAJQ+/WaqMeORnbwmZ+Uh7OtzmzsLePutkpldBACviFtjzhQ4Yx7UiG0mLKmfpfKAEyeb7rRFO8Hh/0mXO9dkBLfx1zVWhUuVwoH7glpMPNkc+HgbzHPdzxbP3aVkGbOr4Pm/toH12pSAnchhBBCjLi1azaTapUvZEzr6P6hXC5qMkwfbN/adSMytoGwg8+CxFwzARL6yLindPwcUec+qgJ3a9XUIm96x2NKHkDg7k6EhExTPhLwhTfH+1MGu0zGqZxMTI/oGJNktbGcdiwsuHfAE0VjVjIb5i82Fyqlc6B43wGfSgJ3IYQQQoy4yi9Wh3/2lpZ2uq2pYDwAjs2bhnNIg2IHuYUeu/2jMh1YemKXyoBpCTkaS2Ws2vNCp/XpgHL2/ph6k2yVitiZezoCd/u5Gyw7cJ+QNqFzl5qG7aYFZN6uQ5dp7yp9nFXnrk1t/wBJ4C6EEEKIEde8dj0ArenZOJKTO90WLJ4AQNL28mEf10CFy0qsSaambWIvXbhdXhMIg1mEKWV0Be5tgTZq2moAKMQaZ3IOOAYYStqfPjRXhTfFe+GpHldMbTALMpE+Li73E5PI/v2tdQM+jQTuQgghhBhRWmvUFpNNDxWXdLvdU2bKHLKrt6HtSX6jXLhURlmZ3t7q28H0d49sCZlkSmWqWqtoD7YP1TBjFnkBURS0XoP+TkyNZD8fzd0z7vbCU4MVbgUZuWJqMABNVrvJtGEM3D2poKyw257zMAASuAshhBBiRG2vbyO/1gSGyZMndbs9bbqZrJrc3opvR+Wwjm0ggqEgO1pMcFgYshZc6q2+3RZuCdlRKgOEzzWSXi1/FQCHcpDXbtWlD2Riqs1+PiJLZaxPGdqCbdT6agd+biAQCrCuzsyJ6JRxb6roqM9PKxrUffSLw9GRdW+rH/hp4jQcIYQQQogB+bqigXFNpmQiZ9ep3W7PnzGVkNXju+Lzr4d1bANR3VZNIBQAoDBoT+KMof1fxOqp+cn54c0jXS7z7JpnuWvFXQAcVXYULjvYjkfG3dcYnqAaebEy2HKZ8sZy2kPmk4pOgXuD1SPekwLeYapvt9mBu5TKCCGEEGKs+npzDYUtpn46ceLEbrePy89kR5KZBFnz5eput482kUFngc/qFx5Lxt1uCelvweP0kJNojhnJwP3FDS+y6J1FAMwumM3i/a42q47CwDrK2CL7mFuLEaV50kh0JQJQ0TS4x2yXySS6EhmXElESY9e3pxWZ8qThlJBhvkvGXQghhBBjVcVX63Ba5QvestJutye4nezINDXfrWOgJaQduKe6U0kJt03sT6lME0C4zn2kWkL+d8t/ufKtK9Fods/Znd8f+nu8bQ0dO8TymHqSkA4Ot/nZqnNXSsVtgmpkfbtDRYS7dsZ9OMtkbIkZ5rvUuAshhBBirGq2gvGg24ursDDqPg15piWk3rRxuIY1YHa2uDAxIqscU417R6kMdNR8j0Tgvnz7ci5941ICOsDUzKn8af6fSHYnd3SBcXo7d0rpL6U6Av8ode5xC9wjy2S0hno74z5+UOcfEKlxF0IIIcRY5gsEcW7dDEBofDGqh/aC/vGmJWTCttHfEtIOOos8GWaDJwXcCX0f2CVwtxdhGu5Smc0Nm7nkjUtoD7UzIW0C93z3HtK9VtBpzUUgOXfwpSZ2uUxzdXhT3DLu0VpBttWD3zy3pEW/QBxSdqmM1LgLIYQQYixaV9nMuEbTNSV5Uvf6dpu7tMzs01hLsLFxWMY2UB2tIN1m8qU3pY8jLF0z7iOwCJMv6OMXb/6CRn8jOYk53Pfd+8K19gA0W119UgYxMdUWLeMeh0WYWvwtbGncAnRpBWmXySgHpBQM+PwDZmfc/c0Q9A/oFBK4CyGEEGLEfF3RwHgri5s6tXsrSFvKLpPDP7evXz/k4xoMO9Au3PoZbH4fvvoXlC/v+0B3Rx936Mi4b2savv71tyy/ha9qvsKhHNwy95Zw6UpYc0TGfbCSeu7lPpiM+7q6dWjM8zU5s+N9Ew7cUwp6XwxrqESuMjvArLsE7kIIIYQYMV9vb6C40WRxvWU9Z9wLxuVR400FoG2UT1Dd3mQCxEIcJhhPSIdld0LkxM5o7Ix7yA+B9nAQ2xJoodE/9J8y/Gv9v3hy9ZMAXLznxexbsG/3nZqsjPtgOsrY7Ix7aw2EzCRe+2Kluq0aX9A3oNPaZTLZCdlkJWR13BDZUWYkRLaf7KXO3aF6js8lcBdCCCHEiNm0YRupftMy0VNW1uN+4zOT2JxqgsW6VWuGZWwD0eJvoa7dBGWFVkMZEtIgFAy3PeyRJ6Kkxt8cDmJh6Mtl1tetZ/G7iwE4eNzBnDPznO47tbeEO97EpVTGzrjrULjFZKeFp5r7Xnjqk6pP+P2K37OhfkN4W9SJqTCyHWXAZPnt1zhaZ5m1r0F7E4kuEns6hQTuQgghhBgxTWs6sufRWkHaxmUmhgP35tVrh3pYA1bR0hFgF7SbBYAIhcDh7Ny7PBo74w7Q3kxWQhZuq2XiUAbuLf4WfvHmL2gNtFKQXMBvD/5t5xaKNrtMBuJUKpNl6s0hXOeen5yPshbb6qtcpj3Yzs9f/zn3fXYfJ/7jRBa9s4iK5orogXvQ31GfnzYuytmGSU8tIdsa4K3fAZqQJkQPJHAXQgghxIioaW4nZYcpX9A5uTiSk3vcN8XroirLZEqDGzf0uN9Is1tBOnGQG/CDv8WUvhx0icm898adCFbQSnszDuUIZ90HM1mzL/d9dh9r69biUi5unXsrGXb3k67sWnR3cueLjIFyODvqvq1zux1ucpPMRUFfgftLG1+ips0s3BXUQZ5Z8wzHPHsMK6tWAl0mpjZuN5l9GLmMO/TcWaalGvythF//HkjgLoQQQogRYSammixoYi8dZWy+omIA3Du2E/INrP55qNnBZn5iDs6yOVC8H5zwZyiZ3ffBSvXYWWYoe7l/WPEhAKdMO4U98/bsecd4dpSxJQ2ss4zWmse+egyA+SXzWbjfQrITsmkPtYdr46dmTu04oME6lyel7wuoodTT6qlJ2RDwAb1PQpbAXQghhBAj4uvtjeGOMrEE7g6rJaTSIdo3bhzKoQ1YuBWkNxOcHhOQpfRjImdPvdxbhqZURmvNunpTrjQzZ2bvO8dzYqoteWCdZT6p+oQvq78E4IwZZ3DKtFP494J/8/NZPyfVnUpJaknnUpnwxNQRLJOBnktlEtKgYCbo3ienjkAvHCGEEEII+HhzLcdbHWU8pT1PTLWljy+ixeUlKeCjfd06EnbZZaiH2G92sFnoSYMAJsPan4WKemgJOVSlMjtbd9LYbjrWTMrouR0nEN9WkDa77r+lf4swPf7V4wDsmr0re+TuYU7lTuK83c/jzN3OBA1up7vjADtwTx/hwL2njLu/1SzS5XDR6mtv7elwybgLIYQQYtg9u2ILL67cTGGLqVH+IJTa5zHjs5Iotyao+taNzl7u4cDdaWXO7UV3YhXOuJvuLUO9CNPaOjPR16EclKaX9ryj1lC/1Swo5UmK3wAiM+5Wr/q+Vozd0byDVza9AsCPp/8Y1eXCyO1wdw7atY7oKDPSgbv1fmhrCLfABCIWh6LXyamScRdCCCHEsKpsbOOqZz8jv6kGpzVh8PpPm5nV2EZeakKPx43PTGR1Sj7TajfjWzc6e7nbmfFChwcIDCBwt9oF+lvMeazAfUfLDoKhIE6HM15DBWB9vbkAKk4txuv09rLjUlj7CqBN60aHJ7a6/b7YNe5Bn7koSEijKMVMHt3evB2tdbfA/MnVTxLQAbISsjiy9Mi+76OtznwFfODp+wJxSNmlMmjw1XdMzrUDd5mcKoQQQojRZHNNK06nCi+81OZ0U5+SxeaaHisEABiX0dHLvXUULsIU0qFwLXqhnRsdcMa98+TUoA6ys3VnT0cN2Lo68zxOTO9ljkFbA7x9BzjdppQnMTO2BaViEdki05qgaj9mX9BHra+20+6+oI+nVz8NwMlTT8bjb4OaDb2PZfVLsOEt2LwcXr0mtlVsh0rk+yGys0w44957aC6BuxBCCCGGVXFWIv6ADneU2ZqcS7s223szLjOR8tR8APybNqGDwSEfa39Ut1YTCAUAKAxa3UHCGdYYeaLXuMPQdJaxA/de69tbqk1NtsNlWlZ6U2NbUCoW7oSOTxmazfk6PeYutf0vbniRmrYaXMrFD1KmwLPnwfM/hyUXRg/I2xpg+X3moiM5BxJS43fRMRAuL7is93lknbtdgy+BuxBCCPHNUdnYxkebaqlsbBvpoQxYXmoCP5pdEu4osz0tjxtPnNlrmQxAZpKbqkyTjVX+dvxbtgz5WPsjMrAu8PvNDwMtlbEC9yR3Emke074w3nXukR1les24J2WbmvtQABKzTRlPLAtKxSq5c0vINE8aSS5zARP5nGqt+etXfwXgu8WHkPfBg1C/BRorINgePSBvqQZfg7no8KaZTwziddExUF07y4RCps889DmRWQJ3IYQQYox4dsUW5t7yBmf+ZTlzb3mDZ1eMrsC1P0qyk8IZ9+8esS8L9hrf5zFKKVzjx+G36rx960fXBFU7yEx1p5JiL/bT02JGPelSKgND18u9pq2Gep/J+vaacfe3QM5Us/qoDoKvObYFpWKV1LklpFIq6mP+oOIDvqr5CoAfFR8OTTtMwA7WhUWUgNyTDP4266IjI/4XHQMRnqBqZdxbdnY8Dsm4CyGEEGNfZWMbVzz7GW3+EI2+AG3+EFct+WzMZt6317cyvtFk3DOnTe1j7w6FWSlUJGUB4N88ui5c7Ix4YVJEu8T+ZtztdpBBHwStspsh6ixjT0xVKMrSe2nHWfGZ6UU/7Vg47i448e74TEy12e0lIxZhKkix2mBagfsnVZ9w6dJLAZiRPYM90iebIN0qTaK5ymSruwbkjRVQsJt5Lttb43/RMRBdV0+1y2ScXvqanCpdZYQQQogxYHNNK1p3XlXR7XCwuaa1zxKT0ai6YidpVucUT+mEmI8bl5lIRVIWxU1V+LeOrsA93ArSm2l6uCuHKc/oD7tUBkwWOTGD/OT8TuePF7u+vSiliERXL/MLtn9ivhfPhqy+++33W5RFmIqSTWeZiuYK3tv+Hj9//ee0BlrJTsjmugOvRX35L8ifAVVfWx14FMz8fveAvGqVuejY+3TY7fsmsB/JoB0iSmWsjHu4VWVhn4dK4C6EEEKMAcVZieQ1VHLRymd5pWRflhbvRVsg2OeEztEqsGlT+GdPSUnMx43PTGRHssm4t2/ZGvdxDYY9kbLAbS2+5E0FRz+LGyJ7pPtbIDFjyDLusU1MrYH6zebnwj3iev9hSdmmVKRxOzRVQUpu+DF/WPEhSzcvxR/yU5RcxL2H38uEms1Qu8EE5HMvh8+fhdYaUwLTVdXX1tj3HJqLjoEIl8rUme/96DEvpTJCCCHEGJCd7OWoTcvZq2oNl3/0N/bZ8RX+oOY/n20fk5NVHdtM0B1IScOZHns5ybiMxI5SmVE6ObXQaQXf/S2TAXAnEy6XaO/cyz3eGXe7VGZSyvieWypWfGq+e1Igc4gC37pNVrvG92HJ+VC+PNxZptZXiz/kZ2L6RB456hEmhBR8/FcT6I+fDeP2htKDwemBHV90Pm/zzo7ym9xRtMpu5OqpWneUyqT2nXHvM3BXSpUM8ssT6+NQSv2vUkorpVZGue1ApdTbSqkWpVSFUuoupVS3pbuUUl6l1M1KqW1KqVal1HtKqcNiHYMQQggxGq2vaiK/yQQhTjQLP/wrExq2c80/v+TH9783piarBoIhkqqt7PG4vielRhqfmcgOK3D3lW/uVj40ksI17spayKi/E1PBZOiV0yxG1LTDnC/FBHR1vjpaA733uu+PcA/3Va/B02ebr64tFbdbgXvBzP5/ehCLtgZ4/x5T3+1OMo992Z0UujsuenbN3pWHjnyI/OqN8PgpsPG/sOldSLfeO/kzzPfmKpOxt1WtMt/dyZBeHP+xD5R9QRcKmDG3Wr3q0+OTcd8IbBjE18GxPAalVAFwNdAc5bY9gdeABOAy4H7gAuCJKKd6CLgUeAz4f0AI+I9S6oBYxiGEEEKMRp9vq6eguSb8e4K/jUXv/YV0XyNt/tCYmqy6o9FHoXUR4inpX0D1yeZ6KpKtCYitLfzzzS/jPbwBaQ20hhcLKrTDq4Fk3MuXw4alJvv82rUm+5wU/17udW11VLeZDiyT2v2mnr5hG7z9u47Me1sD1Fidewp2j8v9dtNSbdohhuvONYSC7JFUwEFFB3Fk6ZE8cPgDZOKE16+DQKsJ8LMnmYC/rcEE5V5rRdTKiKy7XSaTu0ufbRaHVWRv/8qI929qUZ+Hxlrj/hzwaT+GBJAM/KIf+98EfIi5mMjoctuNQDVwiNa6CUAptRG4Tyl1qNb6dWvbbOAU4FKt9Z3WtkeAz4Gbgbn9fAxCCCHEqPD5lnqOshaoyT7/fHY+/AgFLbX8+v2HufKgC/A73WNmsur2ulYKrceSOjH28ovKxjZuefFrXFbGHeC+p97mgL0njfhjjqw/LwwNcPGltgbTi9ydZCa2Ot2w7E7yvvdH3A43/pCfLY1beu+5HiO7TAZgotW5kpDf9EVvqTaB9I7PAQ2uBNMOcigkZZvadGXVp7fWQUo+7pQC7v7u3R371WwwNeF2P/a08aZjjD3WvBmw+T1TLjPxEHMxsHO1OTZ32tCMfaDcSeBwm+e70rS3JCnbLEbVh1gD92e01o/3Z0xKqWzg8hj3nQ2cBuwD3NnltjTgu8CtdtBueQS4A/gB8Lq17fuAH5ORB0Br3aaUegC4QSlVqLWO/7JjQgghxBBbu24byQGTTU89/HB8k3eh6X9/wYyajfy/j5/itr1PxR8KjYnJqlvrWimyOogkTyyN+bjNNa24nQ4aPUk0uRJICbRR1FI7Ki5W7Ey4UznJ8Vs9uftbKtNSbXqRe1Mh0AY6BKEgzrY6xqeOZ0P9BjY1bOr7PDGwF14qwE2yVUtPKAD+Vti51kzktMtk8nYF5xD1M0lIM+0ZX15oJuNqDUf8tnvnl6RsCLSbMSakQ6BLP/b8XU3gXr3O9G1v3GaeQ4DcIbroGCilzEVdcxVUrzXb0vrOtkNspTL3AKsHMKxW69heC+6UUgr4A/Cw1npllF1mYi4wPozcqLVuB1YCsyI2zwK+7hLgAyzHzPTYM8r91/X2BQzgcy4hhBAifkIhTd3ajeHfPcXjKT7+aCpPOQeAw7as4OBtn8a0+uhoULmjhkyf+a/a3Y+OMsVZifhDJj1sd5bJbto5Ki5WtjSacCc/KQ+XHQj3t1Sma/a5rT4cnE5INS0z4xW4r6+zJqZmTjHBur/FZPkLZsKaF6FqdUfGeqi6ydhKZsMRN0LxflCyP4zfu/s+3lTInW4WgfK3dO/HnrOLed500JTI2PXtKQWQmDm04x+IyDp3iKmjDMQQuGutf6q1/rCv/aIc12Id21fQfzqwK6a+PRp7im20TPl2oKjLvj3tR5d9hRBCiDFhU00L6XVmlVFSUsNdWOZe8wuappugat+dazhhz9j+8x9pTes3hn/2TIi9h3teagI3njgTp0OFO8scl8eIX6x8UPEBd664E4CylGLAKpXpb8bdzj4TsoLTRjjo55CQRkmaucApbyiPy5jDE1Ozd4WyOSZoPuVvZqJnKADv/sGUpmgNedPjcp+9yp5ktc90mkx0Vy01kJRhxnr49d0XgXInQPZk83Pll53r20ejru+NOGbch4xSKhVT235TLyUs9mW0L8ptbRG32/v2tB9d9gVAa53R2xdQH8tjEUIIIYbKZ1vrKbCWcvdO6MhQK6VIO2B/ACZXb2J7w+ifmArQXm76gvu9iTgz+5cNXbDXeC7+zuRw4D7e7sgxQl7c8CIXvHIBje2N5CTmcNm0H3fcOJDJqSWz4fg/mkC69CDIMvXsE9LMBU55Y5wCd6tUZlJijmml6E0zge8+Z0NzDax7w0yQ3fIhbPskLvfZq8RMU0sPHX3NI9ktE93JULRX9EWU8nc13ys+h1rrk4nRVt9u6/reSIutu9JI93G/GmgHbu9lH7vvkTfKbQkRt9v79rQfXfYVQgghxoQvtnZ0lPEUd+7Ckre/KSuY0FDB+vLKYR/bQDi3m7KStrwi1AC6fUwvTItYhGlkWmBqrXn4i4f55X9/iT/kpyy9jMeOfoypHutCxJ0Erpg7YneWOcGUzTg9ZlEiCGfctzdvpz3YPqixN7Y3Utli3iuT7LaLCemm3aMnxfQ+d7rNY0jNNxNmo/V4jyelINXqntMYZaEpO5hPLei5LWWe1RbS3wxoM5HVzsKPNpETl51eiJhw3ZuYAnel1Aal1PqIr3UDGWOXcxYClwB/BPKVUqVKqVJMkO2xfs+ko8wlWlf6QiDysmx7L/vRZV8hhBBiTPh8W324C4unuHNmLm1PUyrjRFP14TBkRuMgqcr6r33cwEp7JmQnUWFNSvRv24YOBuM1tD5VtlTyWvlrXL3sam778DYA9srbi0ePepRxKeM6lrEfSLbdphSk5JufG8xzZde4h3QoXE8/UJ06yjisJXHswLGluiP7npJvWi2Ggmb7ULMXIGqMUoTRaK8u2ktJSUouJOeZxZl8jWbfgV48DbWE9I5xJmXF3K4y1inCbxIu2IqbfMCDadN4c5TbN1jbf4tZOHgf4Fn7Rmthpz2ByG43K4H/p5RK6TJBdT/r+9j4F00IIYSwaK35fGsD51mBu3t854y7My2NndlF5FRvw/fpJ8DxIzDK2LX5g2Rb9freftS3RyrOSqLCyrgTCBCorMRd2PeqkwP10Y6PePTLR/ls52fhTLXt8AmHc+OcG/E6rQ/8W+vM94EsvhQprQjqN0OTyT7nJ+fjdXrxBX1satjExIzYWkLW++ppDbSGVyKFjompeYl5pLVbxQiJ1vNpT5D1ppkx+Lt0bxlKvWbcrWC+r1pwp9uswoqGunKYMKdzLfxoUbupY5zV62DioTGNM6bAXWt95iCHF80G4MQo26/H9IC/FFitta5XSr0K/EQpdWNEQP4TIAV4KuLYpzEtKM/FaiuplPICZwHLtNaScRdCCDGmbKltpbm5lRwrIOyacQdomjiNnOptJK79aphH13/b6loptLK3aZNi7+EeKcXrIpCbH/7dv2XLkAbui99dzIb6DeHfPQ4P07OnM79kPqfPOB2HiihgiEfGHTqCWCtgdSgHxanFrK1bG3Ode2ugle8//312tuzkxjk3clTZUUDExNSMiR2rdtqdV+wJssvuhNYGE7RHdm8ZSnbGvbnKdI9xus3vgfaOCau9LVLU1gAb3zbHOVzmE4Nld0Le3cMz/li1NcCKRzvGmZjZMc4+DFFTzr5presxCzt1opS6BAhorSNvWwi8AyxVSt0PjMcs7vQfrfWrEed8Xyn1FHCLVYqzDjgDmACcOSQPRAghhBhCn2+tJ7elDqf1wXe09omOGbvBB6+Tv3UdWusB1Y0Pl+2V9eS2muA2e+rAFxIqyMugxptKlq+R9i1bSdp333gNsZP2YHu4BePZu53N4aWHMzVjKm47qOwqboG7HcRWQjAAThclqSWsrVsbc0vIF9a/EF4Y6oq3rsAf8nP8pOM7JqZmTIJGazXeyJaJJbNNENlSbTLtwxX02o8ZDU07IN26SG2qIFz4kdbLBVpLtZngml5sWkMm5XRepGm0aKk2pTGuRLMIU3KuCeZjKEfq9+RUpdRqpdSvlFIFfe8dH1rrFcB8TMeYO4DzgPuAk6Psfjpwl/X994AbOFprvWx4RiuEEELEz2db68MZapxO3AXd//vN2scsaZLR2kDjps3DObx+q17TUV+dWFY64POURJTL+DcP3WMubygnpE3v+FN2OYUZ2TN6DtrBtFCE+GXcdSicbQ53lomhJaTWmse+fAwAl8NFSIe4+u2reXr10+FSmYnpEztKe7r2Ok9IM4swDWfA6001XWOgc7mMPTHVm2q+epKUbTLYqfmQWdJ9kabRwh5n1kQzoVYR8zgH0lXGj6k7L1dKPaeUOlYpFbfuNFrrQ7TWe0bZ/rbW+iCtdaLWOl9r/XOtdXOU/dq01r/UWhdqrRO01rMjs/JCCCHEWPL5tgYK7Pr2oiKUq/uH5RP2nkmbFUxuXvbBsI6vv+we7u0uD67c3AGfZ0JWxATVrUPXWWZjw0YAEpwJ5Cfn976z1h0Z98iuIQORkGEyshCemGl3ltnU2HfG/d1t74Ynod5/+P3MypuFRrP43cVsazbnm5SYbxYsgpi7mgypTp1lIiaohjvK9FHfbpf5+JqhoaL7Ik2jhT1OHTKLX/VjnP0uldFaz1BK7Q+cA/wAOA6oUEo9BPxFaz3ojjNCCCGEMFnTL7bWc0IPHWVsuRlJ/DerhBlV66hf8TH8+KThHGa/BKzseGNmHqqntn4xKM5K4tMkuyXk1riMLRo7cJ+QNqFzLXs07c0dK2EONuNuB7G1G8LZZzvjXtFcQVugjQS773kUj371KAD7FuzL3vl7c/f8u7n49Yv5oKLjwm6SJ6PjgNGyumhqIdSs65xxb4xxYiqMXJlPfw1wnAP6i9Fav6e1Pg/TZvFczETTK4HVSqnXlVI/siaFCiGEEGKAtte3Ud3cHu7h3rWjjE0pRVXxFPPLl58P1/AGxLndBNm+/MEtZj4hO7mjl/vmIcy4128EoDS9tO+d7Ww7DD5wh27tEUtSO+Y3bG7suTxoff163t76NgA/nm4WhEpyJ/HHw/7IgUUHAlCQXEBG0LrIcCeDa5SEbeFJuVaWXWuoty7M0mJsHzoSZT4DMYBxDqrERWvdorV+UGt9MDAN+DtwCPAosE0pdadSqvssGiGEEEL06fOtJhAsarUWXyqJHrgD+KeaVSNTy9ej2we3QM9QCvdwL4ptpcielGQlhVdPDVZVEhqix2xn3EvTSvve2Q7cHS6zkNFgdWmPmJeUR6JVPtNbnfvjX5lO2eNSxnHI+EPC2xNdifz+0N9zxewr+N2833XvKDMa2BcrrTUQ8IGvwVpQid4npn5LDLo2XSnlVEqdiFn99IeYab9vAO8BFwNfKaW+N9j7EUIIIb5tPt/WAFpT1NJ7xh0gcY/dAXAF/bStWjUs4+svrTWZdTsA8E4YXF4vL9VLbVqOfWL8W4emXCYcuMeUca8z3xPSY15Qp1fhzjI7IehHKUVxqnkP9FTnXu+r55/r/gmYbLvT4ex0u9fp5cfTf8zuubt3TExNGk2Be8Tk68aKjv7tytGxKNW32IADd6XUNKXUrcBW4BnMAkm3AVO11vO11sdgsvCrgFviMVghhBDi2+SjTTWk+ltIsBbJcfdQ4w4wfnIJO6wJkS0rVw7D6PqvvrGVnGaT5U2fPLAe7jaHQ5E4rpCgVXfu3xL/cpnatlrqfSaLXpYWw3jj1QrSlhbRHrFLnXtPGfdn1jxDa6CVZHcyJ06OtlxOhJYorSBHmjelo3NM43ZosC7IkvM6+rp/iw2kHeQ5SqllwBfAZZjVSE8Gxmutr4icnKq1XotpyTgpTuMVQgghvhWeXbGFZWurKWzu6O3sKe454z4xN4VVmSaLXfvRx0M+voHYvnp9uB997rTJgz7f+JxUKq2gcygCdzvbDh0Bc6/Cq6bGKXD3pnaU3FiBu13nHq2Xuz/kD5fJnDj5RFL6KtdptQP3UdBRJlJkbX94YqqUycDAMu73AWXATcAkrfURWutntNaBHvb/ElPzLoQQQogYVDa2ceWznwGEA/dGTxLVytPjMROyk1iVZYLLtk8+HfpBDkDNKtOe0K+c5E4c/BS4yF7u7UMRuFsTU3MTc/sOgiH+GXfo1h6xt4z7a+WvsaNlBwrFj6b9qO9zj8Yad4gI3CNKZWKdmPoNN5DAfQFQrLVeqLXe2NfOWuvlWuuzBnA/QgghxLfS5ppWHFaNtN1RZkdyNptrWns8JsHtpHbCVMB0bgnU1g79QPvJ7uFenZqN0z34xdsjJ6j6h6Al5IaGDUCM9e0QEbhnxG8QXTvLWL3cK1srafG3dNr1ia+fAOCQ4kMoTuv50xkA2lsg0GZ+HnWBu91ZZqu1aioRq6p+u/U7cNdaP6e13a1fCCGEEPFWnJWIP2RW6yywVk2tSM6mOCux1+Ocu0wjYNV8t37yydAOcgCCm02WuCE7PouvT8hOYkfS0GfcY+ooA/FbNTVSZPaZziU7kS0htzRu4cMdHwJw8tRoC8t30RpxYTcaFl+KZD9mX2NHX/xYerh/C8QUuCul5nb9GuqBCSGEEN9WeakJzJlsOqYUWhn3aXtNIy+15wV3AEoKs1ifbgKc0Ri4OytMb+72vPgEYZ1KZYagl3u/WkEG/WBnwAe7amokO/vcWgP+NrITsklyJQGd69yfX/c8YMp67F7tvbIDd4c7Pq0r46lrdt2VMPo+FRghsWbcl2JaPL4R8bMQQggxqlQ2tvHRploqG9tGeiiDlugxbfzK2k2Atds+0/s8ZlJeMl9bE1RHY517uIf7+MH1cLcVZ3Vk3HVDPcHGxricFyAQCoQz2jGVytgTU2FoSmUAGrejlOqoc280n2BorcMtII+deGy3FpBRtUZ0lIlH68p4cid0fg5TC0ffGEdIrIH7d4BDrS/7ZyGEEGLUeHbFFube8gZn/mU5c295g2dXDN1qmsNhU3ULrlCA1EZ78aW+J3NOzEnha2uCasunn6KtcpvRQAeDZNRXAeCN4bHEIsHtJJDfEdjGs7PM1qatBKwyjbKEHKjZAG0NPR/QVg/BdlPeEU+epI7SmybTA9+uc7cz7isqV7ClyTz24ycdH9t5R+vEVFtaUcfzOdq63oygmGaGaK3fHOqBCCGEEANV2djGVUs+o80fog0TrF615DMOnpLTZ3nJaKS1pry6hbyWWpQ27RN7W3zJNik3OdwSUjc10b5pE96ywfVLj5f2bdtxhcwUuYzJE+N23syiPNqcbhKCftq3bCFhet+fTMTCrm93KSeFL18HOgQOJxx0CZTM7n7ApmWw4S1QTvjHxT3vNxCphebCoMGUGtktIe3OMna2fdfsXZmcGWObzdEeuPsazfOJhvqtkDstfs/nGNZnxl0pdaBSKru/J7ZWVD1QKZU6sKEJIYQQsdlc04rb0fm/NLfD0WsXltGstsVPoy8Q7iiDy4W7oO9VI3NTvTRl5tNmLVTTvm5dH0cMn52rzFiCKHKnlsbtvCXZKUPSWcauby/RLlw6aOrXXV5Ydmf3zHtbA6x42CwQlJQJ3uTo+w1UaoHJPu/4EtoawqUymxo20Rpo5aWNLwE9ZNvbGqJ/WmAvvjTaJqaCGeva18zz6U4yY4zn8zmGxVIq8xZwxADOnWEdu+8AjhVCCCFiFtmFxeYLBPvswjJabapuBqDQ6ijjLipCufr+kFwpRVl+KltS8gDwrV07dIPsp5o1pod7ZVImRTlpcTtvSUSdezxLZcITU5UHGreZtoTNVRAKQkt1550btkHzTnC4TG22Oyn6fgPVWmuyz58/BUsuZEKreX9Ut1Xz/LrnafY343K4OLrs6M7HlS+HJ38Cz10ESy40v4fPOUoXXwLzvDk95vlUDkjKju/zOYbFErgrIFspVdKfL6DYOlYIIYQYUnmpCSw8unOJRFlO8pgskwEorzHdSSa01wHg6cdkzom5KWxKNdl539rhybj3NSlY+/20rVxp9k3NJS1h8D3cbROyk6hINoUBcQ3c7VaQeKDdBMo0bIVQuwkkI1V+YUppQiFTm+1vMWU1XfcbiLYG+PKfJvvs9IDLS8lnz4VvvvfTewGYO24umQmZnY976zao2wItO83Y7Kx1MNBRiz8aS2WSssGTDBklkDsdgr74PZ9jXKx/OXdaX/2lB3CMEEII0W+7FnXunb1qRxOrKhrZpWDsVWxuqjaBe5nP1CG7S/qub7dNzEmmPM0E7s2rVsd/cF08u2ILVz37GS6ng0AoxI0nzmTBXuZCo33zZuqeepq6Z58lZedOAGqzC1Bx7BBSnJXEUivj3rZ5cx97xy6ccS+ZA9VLTKkKytRaJ0R8YtDeDJuXQ8FMM3m0ubqjFj4hDp8stFSblo2OjpAtMwSprmQaA83saDETVo+ffHz341rrwC4ha6s1nwa0VIPH17HfaAzcE9LM87fsTvC3muc+Xs/nGBdL4L54kPexfpDHCyGEEH1aV9kEQE6Kl7REF+urmrn/rfXcevIeIzyy/rMDd7vG3VMce+Be1ehjk9X7u3Xdep79cBML9pnQx1EDU9nYxlXPfkZbIASBjknBB6S047/1Jprfeiu8bxDFh/nTeKj4IPJXbAkH94NlMu4dNe46FEI5Yiko6FlTexM7W82FRlnmZCibY4JHp8dk06tWQ65ZpZZ1b5gVSDNK4Ng7TKCZlB2/IDMp22TbUYCG1lqUO4mS5GK+qP0agExvJnPHze1+XLu1gJHDBc2V4E0x263FnEDFt+d8PJXMhry7zYVGPJ/PMa7PwF1rPdjAXQghhBhy66pM4D4lL4Xj9ijiqiWf8dzKrfzyiF3ISxtbJTPlNc2gNel1lUBsHWXABNJ//3Az2VbG3RMK8IfH3uTgXX4wJGVDm2tacTo6Z893rd5A7U+uRdXXAeDIy+Px7D35V/FsdiZlAPHt+JOd7KE601yoqHYf/m3b+lVaFI2dbQcoDSkTsBfOMkF77Qb46h+Qczm0N8GG/5odJ36nY7GkeLKzz8//3ATibfVwyFWUbHw6HLgfPfFo3NaE5DCHE7ImwvZPOj4tKN7PnK/yq45zx9LzfaQkpEnA3sXgLkmFEEKIUcIO3CflJbNgr3FkJ3vwBzUPv7txZAc2AJuqW0hrb8HtM11xPMWxBaKba1rxOB3sSMrCZ5VWlDXuGLLuOsVZifiDHZOC52/6gEVv/AlVX4cjPZ1xd97Jylse4pFdDg8H7RDfjj9KKdwlJfitANS3ek2/jn9z85usrFzZaduG+g0AZHgzyGipMxvTCmHX75mf67fA1hWw7nVTf+1OgonzBvMwelcyGw74mQm8dz0RSmaHO8tAD91kKj6H5ByYfBjM+YX51KC1FgK+0T0xVfRKAnchhBDfCOuqzATCSbkpJLid/OQAE9g89l45Le2BkRxav7S2B6ls9DGuqSq8zR1jqUxxViKBUIiQcrDZmqBaWF8xZN118lITOGHWOBw6xLmfP88vPn4Ctw6yLb0A/x/u4+mUKVzzwlfdJrz5Q6G4jml8TirlKdaE3DWxB+5vbXmLi1+/mLNePIsvdn4R3h6ub08rhUZrtdfUQsgqg0Kr9Oqzp+Crf5ls9qRDwT3EHYwyS8GbCr56AGYXmJ7me+buyfSsKL3rt68034tmwcwfmIsLfwts+XD093AXPZLAXQghxJjnCwTDnVgm5aYA8JP9J+B1Oahv9XP7y6t77Hgy2tiPo7TBBIyuwkKcqbFNsM1LTeDGE2fiUFBuBe7HpbUNaXedrGQvl3/0N05aa9Zq/KhgOv9z8M849tlyrn3hK9oCmkS3A6/LQarXRYLbwY0nzozrmEqyktiYZspU6r/8OqZjQjrEXSvuAiCgA1zx1hW0BsynAOGOMslWhxgwgTvAtONM68evX4DyZbDpXRMUDzXrwoSWagj62bdgX54+7mn+OP+P3Sf7tjdDlfU8FO5patvH7W1+3/hWRw93CdzHHAnchRBCjHnl1S0EQyavOznPBO7ZKV5mlWQA8MCyDcy9+Q2eXRG/doFDxe7hPsla3t47JcaVMC0L9hrPKfsWs8mqc8+r3R7fAXZRvX4T39nyMQCZp/+EzDvvoqVL9jmkYcnPDuShs2fz3//9TtwmptpqW9rZZAXuq95bGdPr/NLGl1hVuwoAl3KxsWEjv/vwd0BExt2TYXZWjo7A2eU1pTL24kDpxfDuH4d+cSCrNz9oc+EA7JK1C2meKDXgFZ+b9pROD+TtaraVzjHfG7dDjdU3ZDQuviR6JYG7EEKIMc+ub0/yOCmwJqJWNrbxcXkdAFpDWyDEVUs+G/WZdzvjPtVq85cwdWq/zzFzfEY4496+fj06GIzfALtwrvoSgKDHS/7//i8FmSkkujuHFx6ng9b2EHtPyIx79r+ysY1/frItnHEf31jJr59Z2evr7A/5+b+P/w+A+SXzuXjWxQA8seoJ/rvlv5Q3lANQ6rDGmpwHTqufh93lxJNiAvfM0uFZHCghHZxe87N1Udcju0wmfwa4PObnjGLILDM/a+v9IDXuY44E7kIIIcY8u759Ym4yDqvLiT1RM1I8J0UOlU3VLaA1461MuXcAgfuk3JRwS0jt88V1YaJIWmsyN5pe8f5Ju6BcLoqzEoe8pj2S/TrbGXd3KEhxS3Wvr/OSNUsobyzHoRz8z6z/4cwZZ7J3vikl+dV/f0Vb0AT9ZSGrBCWtsOPgpGxwJZh+7kV7QrBteBYHUqoj695U2fN+XctkIpVZWfdgu1mAydGlE40Y9SRwF0IIMebZPdwn5aagtSbU3EyRv4HC2i3M2LmegmaTDR3KADJeNtW0kOlrJLHNPCbvlCn9Psek3GR2JHd0lvGtXRvXMdp2NrUzaedGALy77w501NknuIeupj1ScVYiwZCmMjGTFpfJSBfVbuvxdW4NtHLPJ/cAphvLxIQcnHXl3LjvVaS4U2jym+fdoRyMb7ey9qlFHSew2zO2N0NDBfiah29xILtcp7eMe7QyGVvhnma8G96Cze/DK1dD+fIhG66Iv/itOSyEEEKMELtU5uB177Nqr3PQrSbbepd1u185uWD+L7n8zEOHdKJmPJRXN1NmTUzF6cQzaVK/z5GV7CEt2cuW1Dwm1W/Dt2YtqYcdFueRQnllPZPrTDY/d/Ze4e0L9hrPwVNy2FzTSnFW4pA+53mpCdy4YCaXP/UJm1ILmF67iTOLQj3e59++/huVrZW4HW4uyj0Qnj4LtKbIlcBVk0/hqq/uB2Bcyjg8TaaWvFPGHUZucaBYAvdoZTK29mbTh97pBleiGfeyO81jkX7pY4Jk3IUQQoxpWutwqczk5a+Fg/ZIbh1kVuVq9i0d4nKGQQoEQ2ypbaW03gTuntJSHB5PH0d1p5TqVC7jW7curuO0VXz8Od6QabWZsfesTrflpSYMSU17NAv2Gs/J+4wP17lPbIwe2DY0bOWBT+8D4IfFh1P46nVmombTDnAncuza9ziy2Fzg7JYxBUJ+c2Bkxt2WkGbaQw5nwBtZKqO7FiRhVkTdstyUwnQtkwFzoZGYZSbUZk00NfrDUZ8v4kYCdyGEEGNaZaOPJl8AtCZhy0YA8i7/BZNe/A9T3llG8ne+A8AutZv5eHPtCI60b9vr2wiENBOsJekHUiZjm5SbHJ6gOlSlMq2ffAJAfUom7oIhWDW0H/aekBWuc4/ay718OQ/94yc0+JtI0nDups9NT3SHywS6AR9Kh7hh5kXcOvdWfjVxgTnO6R093VfsjHvQB211nW8rXw5PnQnl78HGZSa73lVStsm2p+ZDcrZpdTkc9fkibiRwF0IIMabZ9e05vnpoNj8nz52Lp7QUV1YWSbNMJniX2vJwl5nRaqPVCrKswQrcpw4mcE8Jt4Qcqs4yrtVfAVBTMvBxxsv0grRw4N5eXk6oLaKrTFsDO/97E48FTenL6T5FdvV6E5Q7rU80GreCw4kntYAjy44k22f3b883E0NHg+Rc05oSOk9QbWswJS8BaxXX5JzoLSrt+nzfCNTni7iQwF0IIcSYZte37xuysukuF97S0vDtibvPBKC4sZIv124b7uH1y6bqFhw6xITGgbeCtE3KTQln3Ieqs0zWJqujzNQoK3cOsyn5KWxOt2rRQ6HO5UEt1WTg4FftXqYFFWfoNFMucuivTdDqb4Hmatjvwo4gNrxiapQymZHidHVkxyPr3FuqTVlPeLGogp5LYEpmw4l3w1E3me8ls4d+3CJuJHAXQggxptn17TP9JkjxlE5ARdSFJ+y2G1opHGgCX35Jm3/oepoPVnlNCwXN1XiCprZ6UKUyeSlUJGd3dJaJc517sL6e3DoTPCbusUdczz0QCW4nWePyqfckA13KZZKycTk9nJRUwpPe6aTkTjMB8OTD4AePQslBUHqQWVzJZk8Q7joxdaSFJ6hGZNyTssHfZkp+wHyS0FsJzEjU54u4kMBdCCHEmGZn3Msa7ZVGOwe7zpQUXGUTAZhcs4kvttUP7wD7YVN1M6VWmYxKTMRdXDzgcxVnJuJ0OdmSaiY0+tbEt8595wdmtdSgcpC/955xPfdATStKD09Q7RS42yUiTi8KoL2lo0QkOQdKDzQlM1s/MvsH/dBcZX5OHW2Buz1BNSLjnpBmerQH/YA2JTNSAvONJO0ghRBCjGl2jXte9VYgepY6Zc89qF+/zkxQLa9j7wmjZLJhF5uqW9jLyvR6J09GOQaeX3M5HZRmmwmqk+q3xX2CatXyj3ACG1MLOHj86JjcOL0wlU2pBeyxc133Caq9tXAs2gu2fQw7V5u68LZ6sJeRGnWBew8tIUMBE7wX7wczFkjQ/g0lGXchhBBjVrMvwLb6NpQOkbhtExA9cE+0FgfapbacjzeNzs4yWmvKa1rCPdwHUyZj69wSMr6Be9unnwKwPq+UjKT+t6wcCtML08IZ97bVUTrL9FQikrer6WuONn3Q7fp2T8roC4DtjHtbvSmPAWipMYG80wNl80bfmEXcSOAuhBBizNqw09S3F7TUoHw+wGSqu7InqGa3NbDx6w3DN8B+2NnUTkt7kAlx6Chjm5TX0RKyfV38OstorXGvMR1l6koGPoE23iI7ywQrKgg2NsZ2oNMFhVad/taPIiamjrJsO3Rk3AGarTr3yi/Nd3cyZEwY/jGJYSOBuxBCiDHLrm/f1Wfa/CmPB09JSbf9vFOmoL1m4mHGpjVU1Ld122ekldc04wn6KbJW6xxMRxlbZEtI7fPh37p10OcE8JeX42k2QXFg2q5xOWc85Kd5qc8fH/69X3X946yVX2s3wg4rEB5tE1MBPMnmkwDomKBaaS6iyN0FBlFeJUY/eXWFEEKMWXZ9+x7+GgA8kyehnM5u+ym3m8RdTYC5S105K0fhQkybqlsobtyB06qtjlepTEVyNu12Z5k41bnbCy81uxJIn9r9E46RopRiwoQCqhLTgR4WYupJ9hTwppqfm8ynHqMy4w6d69yDAdhpPc680XMRJYaGBO5CCCHGLLsV5KRmq+95L8FuktWycJea0bkQ05fbGsIdZZyZmThzcgZ9zom5yYSUg81WXbRvbXxaQrZ+YurbV2UWU5KTEpdzxsu0gjQ2pvaygmpPHA4o3NP8HGwHXyN4UuM/wHiwA/fGCqhZb1ZSBZNxF99oErgLIYQYs+xSmfxeOsrY7Dr3KXVbWLmxZugHF6GysY2PNtVS2Ri9ROfZFVv4y7INlFoTUxsKJ6DisFpnaoKbgrQEyq1yGd/afgSymFr2lg8/xF9Z2Wl7s5VxX5VZQmlO8qDHGU/TC1PDde79CtwBxu1tyk82vAWb34c3rofy5UMwykEKt4SshCqrTCa9WCalfgtI4C6EEGJMCoY066qacIaCJFWYVUE9USam2hJ2Nxn3xGA7tV9/jT8YGpZxPrtiC3NufoMz/vI+c295g2dXdF7BtLKxjSuf/YyQJpxxf6M9pccgv78iJ6j2t1Sm5uGH2XTaT1hzxFGUP/I4WmtCPh/tX38NmMB9QnZSXMYZL9MLOyaotq5e3b+Dk7KgahU43ZCYaQLhZXeaFpGjiZ1xb66CHV+Yn/NGfvVaMfQkcBdCCDEmPfD2BvxBzbimKlTw/7d35+FRVecDx79nlsxk3xMSkrATFtkVEEHAfVdQa6u1VVttq3avG7WtXVxrf9bW2late90F17ogooDIGpaw79mABLJOkplklvP7486E7CQwyUzg/TzPPGHunHvn3NwkvPfMe97jATpPlbH2z0QlJgIwqKyAbQe6WHHkOASC8gaPj9oGLy63j3vm51PmcDWNwr+6spBGj3ETEQjcSxL6U1ThDEofhqTGNJVI7E5lmcbiEg4+9jgAyllP3QN/ZM03v0vtF1+Cx/h+F6QPIjXG1tlhet2w9BgK443cdF1Ziae8vOs711eAPQFMFrDFgzUKfF6j9ns4CQTu2nuknrsE7icFWYBJCCFEn1PmcPHnT4xR3wEOI9h1WmxUxCaR3sE+Simix42j9osvyK0s5N0NJaTH20iLtfdYP4sqnLROeGnw+Lj9v3lsKK7G69N4fMZk1JjGelJcxqque+LSyU6KDEofhqTGsNCf860bGnAXFRExcGCn+2itKfzt7zA1uCi3x5GfPJhZJeuJ2bCakp+uBuBAVBIJmelBSekJJpvFjHXQYHyLFSY0DTt3Yknu4gJRUckQmw6+FIhOBXc9mMzG9nASmWjcXPiMGyisUZAwMKRdEr1DRtyFEEL0OUUVzqaAcUCNMeJYFN+P4srO00vs/jz33MpCnv9qX7upK8GUnRSJ2x+YN7dqXyUNHl9T0A4wtNlKmDddd3bQbiiGpMZQGp2Ey2wFwNWFvG/HRx/hXv4VAE+OncPDp32b+0+7npqII/ns2xNzGJgcXvntAUNzUjgQbQTbDe0txNQRexyc8XMjKK47DA11cMbPwi933GSC6LQjk2jjc6QM5ElCrrIQQpzEfE4nJb+6g9JH/hzqrnRLdlIkXq8R9AYWLCqI63fUUeqGYSOb9jE3unC5fcxbkB+0fPLW0mLtDPD3KcKssFlMnDogsU27WJuFu0YYgbUpM5MrzgjeokZD0ozKMk0LMR0lz91bXc2B+x8A4Ot+o1mecQoAy/qP46fn34F11mw8ZguLsyeGXX57QPM8925PUM2ZDHP+BRc+ZHzNmdwDPQyCxrojk2i3vhuek2hF0EngLoQQJ7HK//6Xmg8+oOLZZ2ks7rmR52BLi7WTEW+MSA/yp8qMmT7xqKPUpRmDATCjGVZlnK/VZApaPnlrPp/mkKMRgFtnD2XpXbN58tsTsVla/vfr9vlILzf6E5Ub3JJ+/eLsREWYj0xQPUoge+CRR/GVl1NvsfHU+DlYzEf6euGMUxj6rye59br/Y1W/UQwI0xH3Ef1im0pCOnfv6f4B7HGQNCj8RtoDXDVQsMyYRGuNgth+4TmJVgSd5LgLIcRJyutwUP70M03PnRs2EJGV1cke4cPn0xyuazBWGq0zJg5Onn3qUffLHpDOxphU+tceIreyiE0pQ3D7fEHLJ2+tqLIeR4ORh3zJ2MymG4u/DarH+uRjxDfWgdbYrGYaGo1a3MFYeKk5pVSLCap12zsO3Eu+XI7j7bcAeGnkBdz3/bOZOCCBH760lrzCKr7ceYj6Rg/F/puRgWE64j4qI45fDZrK5zmT+Nn1s4hxuHp0LkOvqy8HSySYHGCLA3s81Bw0tofrzYYIipCNuCulTlVKLVBKFSilnEqpg0qpj5VS09ppO00ptUwpVe9v97hSqs1fC6WUTSn1sFJqv/+YK5RSZ/fOGQkhRN9S8fwLeKurm567Nm4MYW+6p6iyHpfbR7ajDKWNiixdCXjTYu1Ej/cvxFRZiMWkeGDOmB4L6jbvN0ZAI61mBvnrnbu2bmXAE/eTUVdOlNtFlKcBs7Me/NVeoqYEPzXDalYUBhYl2rOH+av2tmkzf00Bm341D4AdCVl4L7+KC07pR1qsnQfnjsWkYM+hOv78yXa0PzU/J0wD99RYG874ZEpiUrn3/a09Ppeh10UlGwF6Sq5RTSZcJ9GKoAvliPsQ//s/DRwAEoDrgCVKqQu11gsBlFLjgUXAZuAXQBbwK2AwcGmrYz4PXAn8FdgF3AB8pJSaqbX+uidPRggh+oIyh4uiCif9TQ1UP/88AKb4eHzV1TjXbwht57ph+0GjlGNgxVRzQkKXVxrNnTWV0mWfkVtZyMzhqcyd2HOfMmzeb9wYjcyIxWxSeMrLKbrtNrTTiTUzk7S770KZzUZjpbCkpmI/5ZSg9qHM4WJjcTWJ/hF3i/bxz5e/YPrIjKYbljKHi6ee+5THHMb384lxV1K0/RBl/pHq3H6xXDUpizfWFPPcV/sAiDCbyIjvmU8qjteh2gacbuNGqMFfanPegnymD0s5MUbeA5Nov/or1B4ygvZwnEQrgi5kgbvW+nXg9ebblFL/BPYAPwUW+jc/AJQDs7TWtf52+4CnlVJnaa0/92+bDHwT+LnW+q/+bS8Cm4CHgTN7+JSEECKszc8rZt6CfCwmE9euf5c5dXWY4uJIv/tuDtxzD66tW/E1NmKKiAh1V49qZ5mxYuo4j7ECqm3o0C6XJYwcNxaANGcVpfuKgdN6pI9wZMR9dGY8vsZGin/8Ezz7D6Ciosj655PYg5zP3p6iCidWs+JQZAL1FhtRngYGOUopqnA2BbFFFU5Glxuj8GWRCexMzCbWn/sfaPOLc3N5b8N+XG4jEM5IsGE2hVcpyICiCidmk8LnPVK1x9rqfPq8nMmQ9i8jPSYwAi9OeGE1OVVrXQ8cwhh9RykVB5wLvBgI2v1eBGqBbzTbdhXgBpoSNrXWLuA/wHSlVEaPdl4IIcJYmcPFvAX5uNw+IqrKuWjnUgBs3/4OMdPPAEA3NtLgXxEz3AVG3If6R9xtw7ueF27PzUVbjZuT2D07qKxrDH4H/ZoC94xYDv7+9zjz8gDIfPihXgnawajA49OAUhT402X6V+1vkdefnRRJ7iFjEufm5EEAbXL/+8XbmT70yKcaRRXOsE0/yU6KRLWqoN+TcxlCJtwn0YqgC3ngrpSKVUqlKKVylVIPAKdgpMYAjMH4VGBN83201o3AemBCs80TgG2tAnyAVYACxnfw/lWdPYD44ztDIYQIvaIKJxZ/nedv7liEzeehyhZD+XlXYElNxZqZCYBzQ9/Ic99RagTuaYdLgO5N6FQREdhHGmUhcysL2bS/+ih7HJsyh4tDDmPC6SkrPqb67fkApP70J8Sde26PvGd7jBz1MShomqB6QUx9i5Hn1BgbYyv3AbAtZRB2q6lN7n+Zw8XSnYebnvs0PVpK83ikxdp56Mox2K0mYm2Wds9HiL4oHKrKPIeRlw7QCPwLIz0GIDBKfqCd/Q4Apzd7ngGUdNAOIPP4uimEEH1XdlIkjR4faXUVXLBvJQCvDzuLH8THAmAfNxb3/v04N2yA678dyq4eldvrY8+hOqLcLuwVZUD3K7FEjR9Lw8YN5FYWsLG4mhnDUoPez8Boe1Z9OeZ//w2AuIsuJPmHPwz6ex3N3IlZfLa1lMJdRknIlEMt/7t07isgvt64gRl/0SzunTOjTZBbVOEkwmxqyhmH8E4/mTsxi+nDUiiqcJKdFBmWfRSiu0I+4g78HjgPuAn4CrABVv9rgc+0GtrZz9Xs9UDbjtrRqm0TrXVCZw+gZ4ZihBCiF6XF2rlsfCbXbV+IVXs5FBnPh4NO96fPeIkca1RacfaByjIF5XU0en3kOI6sNGobOrRbxwic7/CqYjYVVga1fwFb/IH7+fV7wefDnJJCxv33dzkXP9hOH5LSNOLeWFiIz3VkpHzvF8sBcFgjOe+Sae0GucYqsL4W28I9/SQt1s6kAYkStIsTRsgDd611vtZ6odb6OeB8YBJGdRiAwIoYtnZ2tTd7PdC2o3a0aiuEECcdu9fNzOJ1ALiv+Q4ei5V1hVXc/koeBen+vObCQjwVFaHs5lFtP2hkRI6q3Q+AJS0Nc0JCt44R6S8JGeVpoHxLz+T1ByrKTKw0Jn1GT56MKTJ0Qe6E7ISm1UTx+Wjcc2RhovKvVwOwO20w/RPbL/GYFmvngTmSfiJEKIVDqkwTrbVbKfUucK9SKpIjaS7tTSzNAPY3e36gk3a0aiuEECedhtUrsfk8+EwmZv3wWn6dX8GfPtzKZ1vLWLHVw6vKhEX7cG7cSOysWaHubocC+e2nVxsBcdSpR194qTVr//7o+ARUdRWJBTupqGskKTq41XQ2768Brckq3G70c3LPVa/pihH9YnHFJFBjjSLOXU/Drl3YR40CwLrF+KSldvjoTj8RkPQTIUIr5CPu7YjEmEwai1HK0QO0+KuslIrAmGy6vtnm9cAIpVRMq+NN8X/tOwWKhRAiyBo8XjK2rAXAPXIM5oQELhuXidkfpNViYU+8MRWofHVeyPrZFTtKHZi0j2ElRkAcPe30o+zRllKKqGYLMeWXBDcrssblpqC8nuzaMiIcVQBEnRbawN1iNjG22ah7w05jBVVPRQUJh42xrehTJx31OJJ+IkTohHLl1DYzgfzlH68GirTWZVrrauAz4PpWAfn1QAzwZrNtb2Hkxn+/2fFswI3AV1prGXEXQpy0th+o4bQDWwBIPHs2AEWVTiIjzE1ttiXmAFC7bn2v9687dpQ6GFpVjM1VB0D06d0P3AFi/IH7iMpC8ourgtU9ALb689vHHN4NgDk5mYjBg4P6HsdiQk5iU557ww4jcD+83Jis3GiyMGR6aG8uhBCdC2WqzOtKKRewHDgIZGME2VkYCykF/Nrf5gul1DP+138JfKS1/izQSGu9Uin1JvCIv2b7buC7wACMFVSFEOKktXN5HqNcxqhyvwuMUoTZSZF4mk023JY0gMv2LseyYyva50OZwu9DWZfby77yeq48ZASd1gE5WPv3P6Zj2ccaCzHl1Bzkwz2lcFb3KtN0JlBRZqqjEDDSeUI1KbW5CTkJvBdrVJZx7doFQPHSFUQDOxOzuWhQ8KvrCCGCJ5R/lV8GooCfAP8EbsVIZ5mttX4j0EhrnQecg1Ex5jHgZuBpjJH51r4DPO7/+jeMEfiLtNZf9dxpCCFE+HMtWQJAVWIaEYOMiaiByYaB1S93Jhkj7rq2lsa9e0PT0aPYc6gOr08zocwI3I91tB0gcswYAMxo6vPzj/k4ZQ4XawsqW9QzD+S3jywzguNQ57cHNJ+g6ikpwVtbh3udMWH5YE4uURFhNfVNCNFKyH5DtdbPAs92se0y4IwutHMBd/gfQggh/FLyVwFQM35Ki5HfuROzcLm9zFuwicNxaZji4/FVV+PcsBHbkCGh6m6HdpY5sHkaGV3hr9Ry+rRjPpY5Lg5fzkBMhftIKdrFIUcDqbHtFSfr2Py8YubNz8dqNuH2+XhgzhjmTsxi8/5qMusOE1NbBYQ+vz0gLc5OY9bApueuTfnEFRnpPKZx40PTKSFEl4Xf56BCCCGCqvZAKTll+wCImT27zeuzctMAcHk1nlyjyohzQ3jO599+0MGoin1YfV5Qiugpk4/reHETxgNGnvumbk5QLal0csdbG3F5fDgaPLjcPuYtyKeoop6dZbWMOWyUWzQnJHS7znxPGj48iwqbsfBW5VtvY9I+fCgypk05yp5CiFCTwF0IIU5we97/FIB6i43h585o83pmQiQZ8UaFkP2Zxih7uC7EtKO0lgllOwCwjx7d7frtrcVMaFZZphsTVA9Wu7jx+VV4fbrFdovJxNe7D+P1acb6J6ZGnXZqWM0XaD5B1fHJJwDsi+vHuJFZoeyWEKILwucviRBCiB5R+8ViALb0H0VqYuuKuYaJAxIBWB9tTPRs2L4dX31973SwG3aUOphw6Pjz2wMi/RNUk1017N169Lz+MoeLF5bv46LHl7CjtLbN6/WNHnaW1oLWjKswRtzDJU0mYEJOAoWx/oWY3G4AdqYPYVBKdAh7JYToCgnchRDiBOZraCBmk1GXvXxsx2klE3OMwP1TX4p/Rx+uzZt7vH/dUd/ooerAIYZWlwDHVr+9Ndvw4fgijLz2xk2df8owP6+YaQ9+zu/e20xFvRu7RXHz9EHYrSZsFuO/U5+Gp5ftpV99Bcn1VUD4Be6jM+MoTmi5XmHDiFPCouqNEKJzErgLIUQnvLV1lD78CLXL+mZxqvpVq7A2NuBDETWjbZpMwCT/iPtOp8I0YCAQfnnuu8pqGXfYGG3HZiNy4sTjPqayWFDDRwCQUbKbLfvbz3Mvc7i4++18PM1SYzSKm2cOZsmds3nl5qn8/Jwj5SQD9dtrrZFUZ+Qcdz+DyWYxY2o18TjhOOcKCCF6hwTuQgjRicNPPEHFc89ReMedlNY4Q92dbqtaZKTJbE0aQO6IjgPIURlxTaPGNQOHA+EXuG8/6DhSBnLSJEy27lWA6cjhHCPgzq0s5PJ/fMX8vOI2bYoqnLQekI4wmyj55HNqrrycQV99xPRhqURaje9hYGLqttTBFFc3BqWfwdRv7Mimf5dGJjJqXPhMnhVCdEwCdyGE6IDn8GEOv/IqAKqygm/f+1q7QV240lpTvdgI3Ff1G8mY/vEdto2wmBiXlQDAjlSjznv92jy01h3u09t2lDoYH8hvD0KaDBgj6c9VGrndQ6uK8bk9zFuQ36ImO/gXq/L6Wmxz+3xE//cZ3IWFlP7hj6R8PJ/Ad2tMuTHinp8yhOykyKD0NZjGDu9PaWQCAJuTBzEuOyGk/RFCdI0E7kII0YGifz6FqbGh6fmIgzvaDerCVcOOnZhKDwJQMGwCSdERnbafMCABgC/tRnURb0UFjXv29GgfO9LeokbFm3eRUV8BQFQQJqaCMZK+N2UgAHavm4E1B7GaTBRVtPx0JS3WTpq/xnuExYTdauIvE6Pwbt3a1Kb+sUf5p30n2Y1V9KuvBGDWN84jLdYelL4GU2mNi5X9jNKfS/qP47MtpSHukRCiK2SJNCGEaIenvJz6N9/ABLjMVuxeN2MP7eaLkbMoqnCGZTDWmmPhQsBIhUg6ZeRRWsMk/wTVxfWR/DIpCV9FBfWr1/TKQkxlDhdFFU6ykyJZtvMw8xbkYzWZaPT6+NGsIZTXNkDeagBqIqL4yBnL3CC8b3ZSJAdtcZTb40h21ZBbWUBJanabUXKX28uhWiPl5c7zc7lsfCa+v/8flUDEgAFYs7OpW7aMfi/9k+cmT8ENqOhoLrxiZhB6GVxlDhePfrId19g5vDzyfBwR0WxYkM/0YSl94udaiJOZjLgLIUQ7Sv71NKbGBmqsUbw48gIAxh7ejcfrCcvUh9a8DgcVL70EwJKscYzpQipEoCSk2weNo4wyifVr1vRYHwPm5xVz5iOL+e6zqzjjwc/51ZsbcLmNRY0aPD7++tlOXlpRyHh//fb1KUOZ9+7moHzykRZr54G5Y9mZZOT/j6wo4Ffn5bYJYHeV1TZNTL1sfCapNhM1770PQPxVV5L1jyeInj4dAPeqlQBEnToJZQm/8bGiCidWswmUwhFhpAm19ymDECL8SOAuhBDNlDlcLF21g8pXjdz2BcNmsqLfaADi3PU8OimmT4xKVjz3HL7qauotNt4eOoucxKij7pMSY2NAstGuICsXgPrVq3s0z73M4eKe+fm43D5qGzy4fRpfO2+X4qxqqt++Pm14UAPNuROzmHXlOQCMLt9H/4S2N2Zb9tcY/YiJIC3WjmPxF3irqsBsJv7yyzHZbC2Cd4DoMCsDGZCdFInb1zZfvy/ckApxspPAXQgh/ObnFTPj4cV88cfHsHkaqbFGMfvuW8kaPZRDkcbEztMdBSHu5dF5ysspf/4FABYMOZNqWwy/fHNDlybWBtJlvo7JNo5VWoq7uOcm5BZVODnabcHgujL+b8kTxLqdNJgsrE4fEfRAs9/0qQBk1JezfXPbhZi2HDAC95EZcQBUzX8bgJgZM7CmpQE0Be+xF16ANTOTuIsuClr/gikt1s4Dc8Zgt5qItVmwW008MGdMn7ghFeJkJ4G7EEJgjPzOm5+Pva6GS/YYNdvfHT6TCSOzmDokhQ0pRrm8upWrQtnNJpVvvsmB++7DU1HR5rXyp55G19dTExHF/KFnAtDg8XVpYm0gXWahMxZTnBGk1q/uuXSZ9qq1WExgsxhB5biaQh7/+p+kOqtwWmw8MuNmauOSgh5o2keNwmM1Jp/Wrl7d5vVA4D4qMw53aSl1S5cBED93Tot2JpuNrMceY+jni7BmZgatf8E2d2IWS+6czfM3TWbJnbOZOzEr1F0SQnRB+CXfCSFECBRVODGZFFfu+gK7143DGslnuTO5ssLJhJwEXksZwjlFa6lftRrt9aLM5pD19fC//sWhvz4OGDnoOc8+2zTq6z5woCnN563hZ1FvPTIqHUgv6SzgDaygWlbnpnbYKKLWrqB+zRoSWgWowZIaYyNeeblk80KUxUxBQgZXXT2bydNOYf8nn2O//ylocGFOTqbfY3/jl+mDyE6KDProsLJaaRw+CsvmdURt34zPpzGZjMLtWmu2+lNlRmXEUf3Ou+DzYU5MJHbWrKD2ozcZlXJklF2IvkQCdyGEwBj5jXRUc8me5QDMHzqTGnME2UmRDDJFc0+KUVnF56jBtW0bkaNHh6Sf5c880xS0AzTu2k3B9dcz4PnnsWZkcPjJJ9GNjaiUFN4bOK3Fvl1JL8ntF0uEWdHo1bzhTuUG4NCyr+mpseOC8nrO2LWCa3d8dmTj8uepiorC3tAAXi/W7GxynnmaiAEDeqwfALGTT8W7eR3Dy3azt7yOIakxABRXOnE0eAAYlRHblCYTf9llqIjOS2wKIUQwSaqMEEIAcXYr123/lEhvIzUR0XyaO6MpHSMpOoLInGxKI43R6PoQpcuUP/88ZY/+BYCY2bPJevJJlM2Gu6CQgm9fT+3SZVTNX2A0vv4mGixGUBkdYe5yHnN5XUNT9ZR1icZCTBFlBziwq2dy+9cWVDKsysihN8XEoOxG/3z19eD1Yhs5koGv/JeIAQN65P2by5w+BYBBNQfI33Ykr3+zf7TdbjXRr2A77oJCAOKvDEZBSiGE6DoZcRdCCGDpojWct3cFAPrbN/Lxjy5qEeROyElkQ+oQzitcQ/3KlSTfdGOv9q/ipZcpe+hhAKJnnkn/x/+KKSKC7H//m6Jbb8VdUkLRzTcDYM3KYtmIM2DvTpKjI3jq+klkJ0d1KS2iqMKJxV8/fXd8f5zmCCK9jZQuW0HG0OAHz3mFlUyuOQBA0g03kPKjH9JYUEjDju14ysuJv/wKzDHRQX/f9kSNH49PmTBpH6XLV8OMEcCR/Pbc9FgcC4wbI/uYMdiHD++VfgkhRICMuAshBFD/zycwax9V8alM/tnNbYLcCTkJbPRPUK1fswbt8fRa32o+/ZTS++8HIHraNLL+9jdM/hSN6KlTyHnmGUwxMU3tU398O8sLjGBzxrAUJg1M6nIus5FKY4y4e01mtiYNBCBuR36QzqalvL3lDKgxVne15Q5Hmc3YBg8i7oILSLruul4L2gFM0dFUZw8GwJe/vml7oBTk2GQrNZ98AtBjOf9CCNEZCdyFECe9g1+vZvh2o3KK67u3NAXFzU3ITmyqLOOrq8O1ZUuv9a/qbSOnOnLCBLL+8QQmm63F61ETJ5Dz/PNY+vUj6vSpRF90MSv2lANw+pDkbr1XWqydP15xStPzzWlGbr/esP44zqB9Dpcbx5592HzGTVA4jGCbx44HIG3vVtz+ajdb/SPuZ+xZja6vR9lsxF18cai6KIQ4iUngLoQ4qWmtKXzASEHZnZjN1O9d0267ERmxOOKS2B9tBMJ1K1f2Wv9cGzYCkHDVVZgi259cGnnKaIYu/pwBzz3HtrI6alxGMDxtSEq33/Oa03Ka6rknTJkMQOPu3XjKy4/lFDq0vqiKAdVGmoyy27FmZwf1+Mei3wyjnvuwykK2Fx6mut5NSZUTtCb7iw8AiLvkYsz+UplCCNGbJHAXQpzUaj//nNidmwHYOecGIm3WdttZzSbGZsU3jbr31gRVd2GhsUInEDl+XKdtlTLKF3692wiw+ydEkp109BVT2zMrNxWARTq5qXJK/Zq1x3SsjuQVVDHIn99uGz4cZQr9f0mZ/oWYrD4vu5eubspvn3hoB6YiY4Ju0vXXh6x/QoiTW+j/SgohRIhoj4fiRx4FYHX6CE6/6vxO20/MSWSjvyxkfV4e2u3u8T46N2wAwBQbS8SgQV3aZ/nuw0D302SaC+y7vbIR82gjdaZ+TXAXYlpbWMnApsB9WFCPfaysycmUJ2cA4Fizpilw/2bR1wBEnXYa9hEjQtY/IcTJTQJ3IcRJq+rt+VCwDx+KD6bOZfLApE7bT8hJYEOqMeKu6+tx5m/q8T461xuBe+SYMV0akXZ7fazaa6ymOu04AvexWQlEWo1FpkoHjQKCG7j7fJp1hZVNI+724blBO/bxcuaOASBq2ya27K8hs/YQY4qMa534HRltF0KEjgTuQoiT1qFXXwNgUfYkJp81uWmlzI5MyEmk0h5HUYyRRlK34use72NgxP1oaTIB+SXV1DV6geMbcY+wmDhtkHEjsybOKAPZsG0b3pqaYz5mc7sO1dJYW0e/OuMmwxYGE1MDYk+bBEDO/l2s31fOpXu+AsCamUnsWWeFsmtCiJOcBO5CiONW5nCxtqCSMocr1F3psneWbadx2zYAvsiaQLTNfNR90uPsZMbbyUszRodrP1/co330uVy4tm8HIHJc1wL3QH77wOQoMuI7XyX1aE4fbAT+H3hTwGIBralf2708945+NtYWVDLAUYrJX3rSlhs+gfuQs6cDEO1xEbljE+cVrgYg8brrUOaj/5wIIURPkcBdCHFcXl9dyBkPfc53n13JmY8sZn5e8dF3CrEyh4tXX/gIMxovim1JA3h80c4u3XhMyEnkqwwj59u1aRPu/ft7rJ+uLVvAXy/ePnZsl/Y5Ugay+9VkWguk2uxyeDH5U1mc69Z3ef/5ecXMeHgx1z2zgjMfbvmzsbagkkH+ijKW1FQsiYnH3d9gSRk2iMqoBABu2zCfKE8D2O0kXHVlaDsmhDjpSeAuhDhmZQ4X98zPx+3V1DZ4cbl9zFuQH/Yj70UVTk45vAeAvfGZ1FvtWE0miiqcR913Qk4Cm5MHUWszFgZyfLaox/oZyG+3DsjpUmDb4PGyep+RenI8aTIBozPjiLUZC2yXZRmTR50bN3Zp3zKHi3nz82nw+HC5fbg8LX828gqaT0wNn9F2MKrzHBpkTEAd6CgFIOHyyzHHx4eyW0IIIYG7EOLYLcgrwadbbutqABxM3U3VyU6KZPih3QBsSjYqtbh9Pv+qoZ2bkJOAz2Tmq/TRADgWLjzGXh9dU357F9NkNhRV43IbiwYF0lyOh8VsYrI/z31jXBYArvx8tNd71H2LKpy0+tHA5zO2V9Q1sudw3ZHAPTd8JqYGmPwLMQUkXf/t0HRECCGakcBdCHFMHC43/1m2t832rgbAwTI/r5gzH1nMDc+uapGq01kwX3CwmtyKQgB2pA/BbjXxwJwxpMXaj/p+u8pqAfgq00iXqVuzNugLEwUERrcjx47r0s1JIL99WFoMqbG2Dtt1R2Dk/lOv8dVXV0fD7t1H3S8qwkyDx9diW6PXx+aSatYVVoLWDA6zUpDNFWcfuZnISxvOhzVH/9kQQoieZgl1B4QQfdOjn2ynzNGAxQSB+MxqVl0OgIOhzOFi3oJ8IxUDoxN3vrWRZTsP88HGA0RYFB6f5oE5Y5g70Rgx1lrz0ouf8COfkTt++4+v5OFh2V3qc5nDxe/eMxZrWp86jHqLjShPA/v/9wk5118b1HNzl5biOWAEtivs/fj5w4uJMJtw+3wtzqe5L7aXATA+OyFo/QgE7ht8MZCQAFVVuDZuxH6U9JZnm93URdvMOBu9+DT86cOtTMhJILHBQVxjPcBRj9XbyhwuHt/tZWRkAunOKt4eMpMtC/KZPiyl1362hRCiPTLiLoTotrUFlby4wlhF8o7zRzDFn05x2bjMdgPKnlJU4cTaqra5x6eZv66ERq+v3bz7jzcdJGKLMZLtzcxi0sRhXQ7Gmr+f22xlVfpIAGoWfhasU2oSSJPBZuMXa+po8PhwNHg6nEfw+upC1hVVAfDO+pKgTRIe2S+OhCgrKEX1gOH+vnWe576usJI31xrvf9cFubx40xQW/nwmQ1KjafT6WLm3omliqjaZiBgyJCh9DZaiCidWi4V7pv+QX02/lbz03JCkgAkhRGsSuAshuqWk0slPX1uH1sbkxe9NH9Q0Krt5f3BqfHdVdlIkjV7fUdv5fFBYXo/b6+Phj7cxutwYDU6aOrnb7+f2HXm/rzKNhXrM69bgdTi6dayjCQTu3qG5uE0tPxxtHUQWV9Zzz/z8pudurw7aJGGTSTF1kHF9tyXmGH3rZIKqz6e5z/+pxKiMOG6ePoixkW6GpMXw56uOVMYJLLxUHJ3K4cbW2fChFbjOB6JT2JwyGOj9FDAhhGiPBO5CiC6bn1fMzD8vprjSCBrPG5WOxWzitH5GQLO91EG1091r/UmLtXPzjMFNz20WE/ddOgq7teWftkavj78s3MFfPtlOweHapsA9atLEbr/fA3PGYLcYx1+TPgKPxQpuN7VfLjnOs2nJ5R/Vjh43Fm+rGcD1bi/Zicb33OFy86OX1/boJOFpQ43AfRHGwlMNO3fiq6trt+1zX+1lQ3E1AL8/bxAlN93ErjNnUv3+B2gUURFGHfTAxNTChMywG8luus5WE7E2S7fmQAghRE+SwF0I0SVlDhd3z8/H49NYvW4mlm2n4fFH2X7e+SRddR7Xb/sUrY00id4UmISZGhvB0rtmc8MZg5qCrhibBbN/NdSvd5fzryV7yHGUEus2AsWoSZO6/X5zJ2ax5K7ZTB+agstio2CgMUk1mNVltMeDc9MmAGImjm/aHljY1evT/GfZXj7fVspV/1pOfknbTzqCOUJc6zLmA+RFZRobfD6cmza3affyigL++OFWAKK8DZju+QX1q1YBUPrgg/S3evBp4w4jMOK+L75fWI5kz52YxZI7Z/P8TZNZcufsXk0BE0KIjkjgLoToksLyelRjA3eu+S9v/O+33L/8aS7duRRfoVGd5ZvbFzKkqpg1+3o3cN+83xjdnZiT2DQiGgi6XrhpMl/fcxb3XTqqqX1gtL3SFktlUvoxvWdarJ2rJhmB3IeJ/lVUly7F5wpO/fqGHTvQ/mMdzDxSceXp75zK2SPTAPj3kj187/k1bD9YiwKuOS2rR0aIyxwu/rZoJwB1EZEUxRij7odXr23TLpAiY/c0cN/yZ4jaZqTvqIgIvBUVqBee4YE5Y4gya7IdxkTaM8+fGrYj2WmxdiYNSAzb/gkhTj4SuAshumT1vgqm7t/E7OJ12L1ufCi2J+UQ+YMfETF0CCat+em6N1m791Cv9iuQVz86s+XiOM2DrjFZCU0pGqf4A/ftqYMprjz2QHvm8FRMCpaljkSbzOj6euqWLz/m4zUXyG+3pKWxUxuj0cnREZw9Mp0/Xn5K08h7IDvGYlb88rzcHhkhLqpwYjUf+a9iW+IAAGrWbWjTzqc1dk8Df/j6Gcb4v88NP7ublB/fDkDFy//l4lgnn10zmAh/VZ9ZF54RlH4KIcTJQAJ3IcRRHax28Y/Fuzm1dBsAW1MGc+Nlv8f3xH8Y+POfkPGHPwAwrLqErMUf4O7ChNFgaPT42FFqTAodnRnXYbvspMimFI1R/oByc8qg40rRSIyOYGJOIo6IaEoHGyP6jk+Dky4TqNoSOW4sOw8ZueRD0mIAOFDtIiqi5WRVu8VMUYWzR0aIW0/I3Z5kTFC1bt+M1kcS62PtFqzulkH7PyZ9g8zrriHpu98lYsAA8Ho5+MADxBTvA8AUHY21f2bQ+iqEECc6CdyFEJ3SWvObdzdR52rktLLtAAz5xuV88JtLm0Z1oyZOxDb3KgC+tekjNq/f0St921nmwO01gsfWI+7NBSYbZrmrSXdWAXDW1ecdd4A7e4SRtvJZij9wX7wY7T7+ybnNV0wNLPg0zB+4ZydF4vG1vDHqyYonge+dzT8hN1BZRh8+jOfgwaZ2m/dXc922hS2C9rN+eTNpsXZMERGkz7sHgPqvV3D46WcAsA0fjlKqR/othBAnIgnchRAdKnO4eHLxLhZuKWVoVQnxDUYQOfLy89sEvQPuvoPqyDgivY3UPfxgi9HYnhJIk0mOjiA9rvOVQudOzOLVKf4+R0Vx4eUzjvv9A/nmH8Ub9c191dXU+SdjdpVubMS1fQf1a9bgWLyYqvkLaNxrBL/tBe6hqHgyd2IWS++azejMWPbFZRiVdGhZz3356p1cuvcrANzX3cRv/3VPi3SdmJkziZk5E4CGrcYEVluYLbwkhBDhTlZOFUK0a35eMffMz6fRvyzqRXV7AIgYMMBIe2jFHBfHqktv5Nw3Hidp4yocn3xK3AXn92gft/gD91GZcV0aubX4F16KnjABZTn+P3+56bFkxtvZD1QPHkn8nq04PvmUmDO6lrftXL+ekl/8Evf+/W1fNJth+AgK5htlJoemxTa9NHdiFtOHpVBU4SQ7KbJXJk+mxdq5YnwW9+93sCcxm+GH9uDcuJG4C87H7fWR9uHr2L1uPDFxjP75rZhj2vYpfd491C1f3vSphG34sDZthBBCdExG3IUQbZQ5XMxbkE+Dx9c0AXLgHn/Qe+aZHe6XfNEFrPSvJnrwT38K+qJErQUqyoyPNxlVXRobO23vXGtUQok6tftlINujlGpKl1meZSwu5Fi4EO3xdLqf1pry559n37evbxG0q8hILKmpRAweTOrPfkpBvW6qzz4sPabFMUJR8SRwrvnx2cCRlJ68vJ2cu9MYbY+54QbMMdHt7h8xYABJN97Y9Nyem9uT3RVCiBOOjLgLIdooqnBiMZkAY7Q9rqGO4RVG2ceYTgL3Uwcmc/24OYxbtAv74cPUfPABid/6Vo/00efTbNlfg9nn5eyn7qOocA+WzAxSb7uN+MsvbzOi7qmspGHnLgAiJwYncAcjXea/Kwt5PXIoFwLeykrq16wheurUdtt7q6vZ/+tfU/vZIgAihg6h/5//jG3oUJTV2qLt1xuMoD7WbiEttvNUoN4wJDWaAclRTXnurs2b0W43h/79FEN8Hhz2WHJv+m6nx0j5wS3UrVwBPo19zJje6LYQQpwwZMRdCNFGdlJkU4oMwMSy7ZjQYLMTNfm0DvcblhaDKymNrzOMRYlqly7rsT7uK6+jrtHLnN1LsBUaaTye/Qc48Ot72XPJpdR89BHeqipql33FoSefpOSnPzN2tFqJHDc2aP04fXAKNouJ0shEnENHAFDz8cfttm3YtYu9V17VFLTHX345g954A/vIkW2CdoCdzfLbw2ESp1KK2blpbA9MUHW5qF26jJzlnwJQdMFVmKKiOj2GKTqaga+9xqA338BkC/3NiBBC9CUSuAsh2kiLtXPhmH4AKAVTDxllIGOmTuk02DKZFJMGJLI2zUiBqF+xAn2U9JVjtXl/DWl1FXx7mxE0xl9+OfGXXwZK0bhvHyU//wU7pp5O0fe/z+G//b1pBc/o007DZA9eeklkhJkzhqYA8FXWOAAcCz9De70t2mmt2X/PPNzFxSibjYz7/0TGQw92GujuKjNSjYamxXTYpredPTKNQ5EJVNiMnPuSe3+D1eehwhbLoJuu79IxwuEmRAgh+iIJ3IUQ7fL6k6unD0liVo0xoh195tErsZw6MIm8NH+Vlfp66tet75H+bS6p5taNC7B53VjS0kj/zb1kPvwwg997l9hzzznS0GTClptLwtVXk/GnP9L/r48FvS+JUcZo+UvWIQB4y8upX9NyZdHaL77AlW+sJJr973+TcOWVRw1gd5YGRtxjO23XmyYPSiLKZmFbkjFBWVeUA/DBKecxbmi/UHZNCCFOeJLjLoRo17rCKgAus1Wjq4x/d5bfHlDf4KHSHsfu+EyGVO8n7+2PmDFlctD75/nyc6aUGmUF03/9a8wxxqi0bdgwsv7+dxp278ZbWYl95EhM0e1PlgyGMoeLDzYeMP4dncSOhCyGVxVT+sH/GOw/b601h/7+dwCip08neuqUox7X7fWxr9xYfGloeviMuNssZmYMS2HbphymHdgEwGF7PJ6LLsNskpF0IYToSTLiLoRoo6zGRUmVE4CRhUZwFjFoEBHZ2Z3v53DxzDKjBvmaNH++95KllDlcQe2fp7aW2Z++ZBx/3GRizzu3TRvbkCFEnXpqjwbtYEzkjTAf+VO6tL+RLuNadCRdpnbRIhq2GDcZqT++vUvHLSivb1pcamhq+ATuAGeNOJLnDvBa7tnMHJPVyR5CCCGCIWSBu1LqNKXUP5RSW5RSdUqpQqXUa0qpoe20naaUWqaUqldKHVRKPa6UapMYqpSyKaUeVkrtV0o5lVIrlFJn984ZCXHiWFdUBUCExUTUeiM3vCuj7c2D2LX+dJnBVSUU7SoOav8K//JXkpzVNJgsxNx5d0hzprOTInE3W8l0WaYx8VVVlONctw7t83HoiX8AED3zTCLHjWuxf5nDxdqCyjY3N4H89kirmf4JPbMq6rGanZvGluSBrE0bzur0ESwaOIUZw1JD3S0hhDjhhTJV5i7gDOBNYCPQD7gdWKeUmqy13gqglBoPLAI2A78AsoBfAYOBS1sd83ngSuCvwC7gBuAjpdRMrfXXPXo2QpxAAmkyUxJNNGwyRty7kt/ePIjdmjyQeouNKE8DadvXwYQ29+THxJm/Cdfrr6KA10eex+/HhbYWeGAl07ve3ojbqzkYnYxz4FAi9+2i5uNP8JRX0LDNmNybevuPW+wbWOTKYlZ4fZoH5oxpWm00kN8+NC0GU5iloKTF2RmZncy9plsAmJiTQHxk26o4QgghgiuUqTL/BwzQWv9Ea/2M1vpPwAzAihHUBzwAlAOztNb/0lrfixHgX6KUOivQSCk1GfgmcKfW+k6t9VPAWUAh8HDvnJIQJ4Z1hZUAnFO7B7RGRUYSdVrHZSADAkGs3WLCa7awIcUI1k2rVwalX+7SUopvuw3l81EQm87m6ZcQYQl9xt/ciVm8c+uR1VIts43JsY5PP+XwE08AEHPWWTgGDmVtQSWl1S7+t3E/v3pzAw0eH3UNXlxuH/MW5DeNvO86dKQUZDjKiD9SmWdjcTXz84L7qYoQQoi2QjbirrVe3s62nUqpzcBIAKVUHHAu8GetdW2zpi8CjwHfAD73b7sKcAPPNDueSyn1H+B+pVSG1vpAj5yMEM2UOVwtlqJv/TwUdGMjPpcLc1zcUdt6vD42Fhsrko7y57dHT52KKSKiS+81d2IW04el8OTiXazdk8vpBzdT+9VXaK8XZTYf8zn46uoo+uGP8JSV0WCL5MHTvs2U7KRjPl6wje4fz4DkKArK68kbOIEpgKesDE9ZGQAbz7maXzy8GK2Niae6nWNYTSaKKpykxdqbRtyHhGHgXuZw8cX2Q03PPT7NvAX5TB+WErKfcSGEOBmEVVUZZSSqpgMb/JvGYPRxTfN2WutGpdR6YEKzzROAba0CfIBVgALGA20Cd6VU1VG6Fd+13gtxJPXBbFJ4fJpzR6axcGsZ1nZSIXqL1pp93/wWjQUF5Lz4ApGjR3fafnupA2ejh4v3Lidm8xIAYrqQJtNcWqydn5+by+WLR8IG8FVV4dqyhchjXClTe72U/OKXNGzdCmYz/5j5PQqiMrgpM7x+PacOSqagvJ4v6u2cOWJEU4pMxKyz+OU6Jw3NFrUCo0a+bhbBN3h9ZCdF4vVpdofxiHtRhRObxUSj98j5NL/pEEII0TNC/xlzS9cB/YE3/M8z/F/bGyk/AGQ2e57RSTtatRUi6MocLu56eyMNHh/1jV4aPT4+zD9IYwepEL2lYccOXFu24Kur48C9v0G73Z22X7+rlJ+ve53bNy4ArwfbsGHEXdp6OsnRxUdamXbmOIqjjcWJHEuWHlP/yxwu8ufdR+2XXxrHvefXLIwaCEBmQngFiVMGG58ArNlXScy5RyrdVF9zA5ZWeerREWZunz0Uu8VEYG7tmMw40mLtFFfWNwX5w9LDp4Z7QOsJuQBun3HTIYQQoueETeCulBoB/ANYBrzk3xz4X6ChnV1czV4PtO2oHa3aNtFaJ3T2AKq7eSriJFVU4cTXKv8hprGe7275iD8tf4psRykW/6hkb6r76khWWsPWrVS88EKHbd0lJWTf93POKzQ+5Iq94AIGvvZqU4307vru6QNZm26UhTywcPFR27eusPLG6iIe+v7vsb77FgBbZ17On9Twpva3v7IurHKrpwxOBqC2wcOBmRcRPe10Un/2U7JOHdNmtN2rNdefPoAld83m1lnGwk3ri6spLK9nV5kx2h5hMZGdGH7BcNNcBquJWJsFu9XEA3PGyGi7EEL0sLBIlVFK9QM+BCqBq7XWgf/hAhFOe2us25u9HmjbUTtatRUi6CKtpqbVRqPcLq7YvYS5u5YQ7TGC0LtXv8wvz/5Zr49K1i03Andts6EaGjj09yeIPfdcIgYMaNluxUpKfv5zUisr8aIomHsDF91/x3GVWsztF0v92FNhzzIs2zfjra7GHN9+esv8vGLmzc/HZFI0enzkJEWSmr+a32x4F4BlmWN4IOEMdP7Bpn0aPL6wyq3unxBJdlIkRRVOVlZ4ueXZZ5teG5AUxa5DdVhMCotZtQh0f3r2cBbklbC/2sW/luwmJ8modjs4JRqLOWzGV1oIzGUI9fwNIYQ4mYT8fwSlVDzwEUYu+fla64PNXg6kuWS02dHYtr9V247a0aqtEEH31a5ylPZx5c4veH7hA1y/7VOiPS7qLHa8KAbXHOC6nZ8TZ++9snm+xkYcq1YD8M9TLqMsKhHd0MCB3/4O3Sy5uvLVVyn8/vfxVlZSHRHFvdNuJvXm7welPvoZV52H22TGpDVLX/9fu6lCZQ4X8xbk4/KnGXl8GtOuHdy95mXMaLYlZvPoxG+hVds/WdYQfIrRmSmDjFH3lXsqmrbtr3Ky+7CxCuod5+ey5M7ZLeY6RFhM/GCmMer+1ppivtp1GDBKQYaztFg7kwYkStAuhBC9JKSBu1LKDrwPDAcu0Vpvb9VkE+ABTm21XwTGZNP1zTavB0YopVr/TxdYW3wDQvSg+etKuGjv13x/8wfENtaD3U7yLbeQ9fHH1F5+DQBXbF7Ie28ePWUkWA58tRJTo5FBtiRtFH8fNxeA+pUrqZ4/H+12c+C++zj4+z+Ax4N74BB+MvNn5PfLZUz/4Ez8PHvCALalGkHphvkfc+Yji9uktxRVODE3ywFPra/k91//B7vXzcGoRH4/5SYaLBHYLApbq/KP4ZZbPWWQkee+al9F0ycw76wvQWtIjo7gpumD2g10rzktm5SYCBq9PpbuNAL3YWnhl98uhBAidEK5cqoZeB04HSM9ZkXrNlrrauAz4PpWAfn1QAzG4k0Bb2HUgP9+s/ewATcCX2mtZcRd9JitB2rYeqCGCwqMVUajz5zBsEWfkfaLn9MvK53Jf7ib6tT+WLSPuL8/RH1d70xQLf9iGQC74zOptsWyJn0kS3MmAlD68CMU3ngTVa+9DkDsueew+Pb7KYtOYmRGLJERx166sbmK+kZWpxp56RNKt+Nq9LaZpJudFEmD28iQi3K7+P2KZ0lqcOCJjOb+GTfjjU/EbjXx4NyxPDg3vHOrp/rz3B0uD1sP1KC1ZkFeCQCXjsvE2kHqi91q5nvTB7fYlhrbXvafEEKIk1Uoc9z/AlyGMeKepJT6drPXarXW7/j//WtgOfCFUuoZjJVTfwl8pLX+LLCD1nqlUupN4BGlVAawG/guMABjBVUhesyCdSXk1BxkaLURoKX88EdYkpObXjfZbGQ+eD8137+RAZUlLLnvUS7487093i/v6hVYgbzUIxM6/3nKpZxZvRtfdTX1a4xJqCm3/oiU229nzXNGWs2E7MSg9aGowsnG7FNg0wekOqsZe3g3e7NGtCgdmBZrJzPBTsnhWn69+kUG1RxAm80M/ucTvDJ6fJs86nDOrc5OiqJ/QiQlVU5W7ClHa9jpn2w6d2L/Tvf99tQcHv9sOy6PMVJ/3/ubsVtNvV5CVAghRHgKZarMeP/XSzGqyDR//DXQSGudB5yDUTHmMeBm4Gng6naO+R3gcf/Xv2GMwF+ktf6qJ05ACACvT/POuhLOKsoDwJqdTeSE8W3a5Uyfwo6ZRlnF/h+8yopPV/RoaUjn4Qoi9uwEYH36cOxW49e9MiKW8htuA0DZ7fR/7P9I/clP0CjWF1UBMCEnIWj9yE6KpCAmjS1JxmTYi/Z93Sa9pbC8nsIKJ9/f9D4Ty3YAkPnHPxI9dWq7edThnlsdSJdZubeC+euMtKChaTFHTT9yur00Lz7T6AlNCVEhhBDhKWSBu9Z6ltZadfAY2KrtMq31GVrrSK11utb6J1rrunaO6dJa36G1ztBa27XWk5uPygvRE77adZhDNU5mFxuBe/yll3Y4qfOMP95DUUwqFu2j/Lf3MuvhRT1WzvCjF9/HhKbRZOGXv/gG//3eFCYPNALKX1VlkvrCSwz58APiLrwQgD2Ha3G4PAAMSI4KWj8CpQM/GXwGAGfsz+enE5JbBN3vb9xPRu1hLt1rVMBJ/sEPSJg7J2h96G2BdJlVeyt4b72RpTdnQv+jTvYtqnA23WAFhNvkWyGEEKET8qoyQvR18/OKOaV8L2nOKgDiL+t4sSIVaefxSd8EYHBVCUMP7OqREdWyGhdFnxkLFlUMHskZo/szaWASj149DrvVxIFqF48UWtnoiaLM4UJrzROf72ra/7qnVwb1hmLuxCz++H8/ptYWbeT5f/FRi9ffW7+fb+5YhFn7sGZmknrbrUF771AILMRU7XRTXtcIwBUTOk+TAePTCU+rxQDCbfKtEEKI0JHAXYjjUNfg4ZPNpZxVtBYA+7ixRAwc2GH7ogon+/oNZle8EcSdWrot6COqZQ4Xd7y1gTEHjSJNwy46u+m1nOQofnGuke/+xtpivvXUCqY+sIgx933MO+uPzN929UCKRnpaAjUzzwMga9knOOqNajfbDzqo2rOPs/3fw+Qf/AAVERG09w2FnKQo+sUd+URhUk4i/ROOHnzLwkZCCCE6I4G7ELRdsbOr3lhThMflYkbJRgDiL72s0/bZSZF4fZrV/tVETy3bFtQR1fl5xUx/aDE71m0jo96oI548c3qLNpeOzSSQsNHo9eHTUNvgo7WeSNEY/6MbAUirr+SLl98D4L0NJVyz43PM2oclox8Jc64I6nuGglKKzIQjwfbGkqouf4Ixd2IWS+6czfM3TW5T710IIcTJTQJ3cdKbn1fMjIcX853/rGy3xnhn+/3xgy1MObiFaI8LbTIRd9GFne4TGFFdnzESgEE1B7lvSnBW/SxzuLhnfj6NXh8T/BM8qyOiqcka1KLd/mpXm1KPEWaF1dwy/7onUjSSRw6jZPApADS8/RZaa5Z9lc+5hUY1m5Rbbunzo+1gXIv8kuqm526v7tYnGOE++VYIIURoSOAuTmqBYLfB46Ou0YvL3bUUkbIaF3e+tRGfpqmazNr0EVRYjz6pc+7ELJ744/XUWo2gOGHz2uM/EWB3WS1urzFyPqHMqCazKX04xVUNLdplJ0Xi0y3zqE0mxa8vGtkrKRop1xo5/iML8nn1vZXMWPU/LNoHqWnEX3ll0N8vFIoqnNgsLW+OZJKpEEKI4yWBuziptRdIdRRgBdJp9h6q5cev5uHxaeIa6jitdCsASwee2uXALCctjtLh4wCoX7LkOM7A4PNpnlm6F58Gk/Yx7rAx0XRd+rA2o+Yd5VHfcMagXknRGPuNS6mJisOEpuIf/+C8AmO0Pf2WmzGdAKPtEJhk2jL9SCaZCiGEOF6hXIBJiJDLTopsGqUOaPS2DbDm5xUzb0E+JqVwNnoJjFfP2L8Bi/ZRb7Gxot8o/tSNwCzl7FmweQUDCraw/3ANmSlxx3QOZQ4XD3+0jUXbygDIrS4h1m3cQJx//WXtjprPnZjV7iJGabH2Hk/PMEVEUHv2xcS9/ypnFxoLQJXb49g2bBp9twBkS4Gbo3kL8rGaTLh9PplkKoQQ4rhJ4C56VZnD1SZYbG9bb0mMisBiUjR6j6SOTMxJaNGPMoeLeQvycblbBviXjs3grKVGmszXWWO57+pJ3er/uLkXsO9vDxHlaWDZ/EV845buh63z84q56+2NuP39P31wEg+Ub8YFmHJyuOz8SR3u2xtBekcGfvdanO+/htl/C/TW0Fl8/OF2zhidecIEtx3dHAkhhBDHSgJ30Wvm5xVzz/x8wFht9OKxGZg8bho+/IAETz3vDZvJH66a0KtVNNYVVjUF7VeM788760tYU1BJUUU92UlGvnpRhRNTq4VzoiLM3DjETmT5PgDm3Pl9+nez35H90qnIHETS/r2UL/4Suhm4H6w28uwDdb+tXjenvfMMrj3GIkYJs2d163i9qSo2mfx+I5lycAsVtlg+GjiVCH+K0okU4Iby5kgIIcSJRwJ30SsCo9YN/vXcTT4v9e++w3XbFpLurATA0uBintnC9GHBqbLSFUt3HgIgNz2Wh64cw8q95RyodvHE57t4+KqxACg09Y3eFvv5tCZ5/dfUA+akJDJnnnFM7x89Ywa8vpcBuzew51Atg1NjOm0f+HTC7fXxm3c2NQXt/WsPcc+qlxhSY9Rij542jZRbw3cRo+ykSO4Yeylmn5d3h0ynwRKBkhxwIYQQolMSuIteUVThxGJSoDUz9m/g+q2fkF17qEWbb+xczPoBY3t11HXJzsMAzBiWgt1q5rbZQ7n3nU28lVfMj2YNITEqgl+9tbGpfXSEGa/WPDBnDPz1NQBizpqNMpvbPf7RDL3kXIpef5FBNQf59IsN/PDqjm8A5ucVM29+Pj5t5OEHzC7K48fr3yLS24gXRcytt5F9+49QpvCde54Wa+f2G85h3oJ0rCYTdskBF0IIIY5KAnfRK7KTImnw+Lh2+0Ku3/YpAD4US7Mn8Oqw2dyz+mUGOEr50cpXyYr5Zq/0qaq+kY3FVQDMGJ4KwDdOzeafX+ympMrJ/f/bysFqJ3sO1RFhNvHEdRNIjraRnRRJksfJzjXGxMrYs87u6C2OKnrCeBojo4lw1lH86WL0VdNQrdJywP+Jxfx8XJ4jAbsC/ujbwqS1rwBwODKe2jt+x4XXdl5LPlxIDrgQQgjRPeE7JCdOKGmxdnJjFFfu+hKAdem57Hvo36Q9/DClyf154rRv4UUxsOYg+x77e6/06atd5WgNERYTkwcmAca/f3L2UAAWbiklv6QGgKsnZXHeqH5Ni+LUfvkl+HyoyEiip51+zH1QFguWyVMBGLRrA2+tLW63hnxRhbNNQH9OaT4T338OAO+E0xj23oI+E7QHyEJDQgghRNfJiLvoFSVVTnLXfk6UpwGvzc5ZrzxFv/5pAMaoa3k9X9Xs4Mw1/yP67Vcon3sJyRPH9WifAvntkwcmtVhJdMawVBTQfImit9cV89NzhzUFmLWLPgcgZvoZmOzHF3Rmnn8WB79cxPhDO/n22+vQFisPzBnTYpKu8YnFkTz7sYd28eOVL6O0JnLCBHKee+q4+yGEEEKI8CYj7qJXvLumgMt3LwUg6eqrmoJ28I+6Dkzi/D/fS2FcOmbtY/vP7mDtztIuLxHfXVprlvrz288cnoLn8GFcW7fSWFzCgeIyolrd0jZflMnnclG7bBkAMceRJhPQMGEyAFGeBoaV7W139Vab2UxgvD239iC/Xfk8Vp+XiCFDyP7nkxK0CyGEECcBCdxFj9NaUzD/fVKd1fiUiZQbvttuu4EZiRy+7S68KBLLivjfnfdz5iOLmZ9XHPQ+7TlcR0mVEYjPiHGz+7zz2TtnLrvPOYeoqy7grbd+xRsf/oYrdhmrmjZf9bLu66/RTieYTMTMmnncfSkxRbMnoT8Ap5ZuA9qu3vrexv14NeQ0VvF/ec8R7XFhSU8n5+mnMCckHHcfhBBCCBH+JHAXPS6/uIoz1n4CgJoxi4isjuudn3X5LN4eNhuAy7d/jq22ps3oczAs3WGkyaTE2Ih/+2V89fVt2sS6nXxvy4cMdZa1qHjiWLQIgKhJk7AkJh53X7KTIsnrNxKAKaVbAHC3Wr31zTVFmLSPB9e8gKmiHFNsLNlPPYU1M/O4318IIYQQfYME7qLHLX3rU4ZVlwAw8Ec3d9p2f7WLBWPOx2GNxOrzcl7Bqjajz8EQKAN5cbKX6nffBSDt7rsY8tlCBs1/m5znn8eUlY3F5+WfZZ8xZ4IxIq69XmoXfwFAzNlnBaUvabF2Jl17OQA5jjIG1Bzgion9m24Uth6oYWNxNWMO7yaprAiArCeewJ47PCjvL4QQQoi+QQJ30aPcXh+J778JQPWQUURNGN9p++ykSFwmK5/lnArARfu+xuv1BHVhngaPl693lwNwwcaPwePBkpFB4rXXEpGVhX3UKKKnTqH/734LgGddHtXvGMG9c8MGvOXGvrFnH39+e8CFV5+NKdO4OTizeAOr9lbg9S+u9MYaI1i/9PAmACLHjSN6yuSgvbcQQggh+gYJ3EWPWr5oDRP2bwYg45abjto+LdbOA3PH8PHgaQD0q6/kr4NcQS0X+PnWMpxuL/3qyklY8hkAKT+4BVNERIt2MTOmE3vhBQCUPfII3qqqpjQZ2/DhRGRnB61PSikSL74IgJkl69lTVssnmw/S4PHyzroSrF4Pk4vWAxB3ySVBe18hhBBC9B0SuIeZMoeLtQWVLXK629vWVxz4j1FnvDw+jZxLLujSPnMnZnHXzeeRlzoMgMHLPw5af+bnFXP7q+sA+OaOReA1Rtvj585tt3363fdgio7GW1lJ2V/+Qu1nRuAerDSZ5uL8gXv/usMMqS7hH4t38dmWMirr3Zx2aDvW+jowmYi7sGvfRyGEEEKcWKSOexiZn1fMvAX5WE0m3P4l4IE225rX9w5nr32ynpH5RtnENwZNp2zDgS73/bzR/fhB7gwmHtpJ4/KvaCwu7nRSa1eUOVzc9fZGvD5Nv7pyzi40Vj61fffGNqPtAdb0NFJ/9jNK77+fqjffatoee/Y5x9WX9thyc4kYNIjGvXuZWbyeZxOyuPedfACurjI+tYieOhVLSkrQ31sIIYQQ4U9G3MNEmcPFvAX5uNw+HA0eXG4fv3hjA794Y0OLbT1RYaUrfPX1uMvKmh6eQ4fw1tZ12L7M4aLq0UeI8HmosUbxSdakbvXdajYRO3s2h+3xKK2pev31Y+57mcPF4u1l/PiVPNxeI2/8mh2LsGgfh6MSOHTGeZ3un3jtt7CPGtX03NKvH/bRozrZ49gopYi7yBh1P/vgRtCayno3kW4XQ3evByRNRgghhDiZSeAeJooqnCjUUdv1RIWVo3Fu3MiOqaez68yZTY+dM85k5+mnU/HCC+3us//9j5lZZKSk/OeUS2iw2Lrd93NOyeSjgVMAqHzrbXyNjd3u+9trizjjoc+56bnVrNxbCUC/unLO8Y+2vzXiHLL7xXd6DGU20+/394Eyrk/sWWeh1NGv1bGIu+hCAJJqKxhRWQDA1IObsbgbISKC2HODP9IvhBBCiL5BAvcwkRlvx+X2tthmNSms5pYBYoPHF9QKK11R9thj6HaCZu12U/rgQ1R/+GGL7Z7KSmxPPArAmrRcPs05DWi5iFFXzMxNZdHgqXiUCV9lJY5PPulWv7fsr+aOtzbi9mq0f5tJwXU7jdH2Q5EJzPrpjV2a+Bo5Zgxpd9yBbeRIkr5zfbf60R22IUPwDRoKwMzi9QDMLjZugDynTcMcG9tj7y2EEEKI8CaBe5hYuLW0KbiMijBjt5p4+KqxPHzlWOwWEyZ//J4cE0FiVPv52D2hPi+P+q9XAPD3Kd/mtgvuZvef/8PgD94n6lSjZOOBu++hfs2apn1KH3gQKsqpt9h4fPzVxNgt2K2mFosYdUWc3crw0YNZnnEKAJWvvHrUfcocLtbuq+A/S/dw1b++xqdbvj65ci/n7FsFwMCf3sqcKYO73J/km25k8IL5RAwc2OV9jkWMf/LpjP0bSXA5mFi2A4CEyy7u0fcVQgghRHiTyalhoKKukb98agRnl43L5LvTBpKdFNkU5E4flsKirWXcMz+fA9Uunl66h1tnDe2Vvu3/2xMAbEvM5n/9xoFS/GpVNUtmTSTrib+z79rraNyzh6Lbbmfgq6/QuHcvNe+/D8DTp1zK8DFD+OV5uS3OpzvOGZnOW4Omceb+jTjXrcO1bRv2ESPabTs/r5h75ufj8emmGujN2T0N/HCVEfzbx40l+/pru92f3pAx5zJ2P/kEya4afrz5Xczahzcyiv7nS5qMEEIIcTKTEfcw8Oin26l2uom1W/jtpaOYNCCxRZCbFmvnW5NzuGHaQAAe/2wnawsqerxEpHP9etwrvgbgldxzm3K8Lf5cdXNCAtlP/RtzcjK+6mqKbvkBB+67D4AN6cP5eMAULhuX2eZ8uuOcUenkpwyhMDYNgMpXX2u3XZnDxT3z82nw+JqCdpOCX503HLvVRKzNwi1b/0d6fQUqIoLMBx5Amc3H1KeeFpGdjX2MUVFomr92e9L552GyB6+WvRBCCCH6HgncQ2zpjkO8srIQgF+cO5yUGFuHbX91fi79EyJp8Pi4+l9fc8OzqzjzkcXMzyvukb4d+uc/AdgZ35/V6SObttc1eCiva2BtQSVV8Slk/+tfqMhI3MXFeA8dxmeP5P/GXYXJpDhnVPpx9aF/QiSjMuP5cODpANS8/3671Wzam/QaHWHh9CEpLLlzNi9ONHPh7q8ASP3Jj7ENGXJc/eppcRde2PK5VJMRQgghTnoSuIfQ22uL+M5zRr61AmJsnWcuxdgs3HFeLgA+TY+WiHTm51P35RIAXhlhjLbbLMaPiwZueXEt1z2zgjMfWcxH7gT6/+UvYDJeX3HedZRFJXHawKROb0S66txR6XyePYlGsxVffT0lb7/Tpk12UiRur6/FtsBk2BSTl9i/PQQYKTJJN9543H3qac0XWTInJxM9dUoIeyOEEEKIcCCBe5B0d3XTMoeLu+fno/2p2Br4zbubjrp/dnIUFlPLSjM9USLy8JPGaPue+ExW9BvNz88Zzis3T+Uv3xjX1Mbl9jXdODhPO52cZ54m/f77eSLamEx6wSn9gtKXc0elUxsRxZeZxntv/OfzbT5liLFZmopp2q2mFpNhy/7yF9wlJUaKzIMPhm2KTHPWjIymyb9xF12Essh0FCGEEOJkJ9FAELy9toh5CzZhNZvwdHF1091ltXi8LSdQBgLwzvLBs5MiMZsUnmaTL7tbZvFoXFu2ULt4MQD/zT2X9Hg7P5g5GLvVCHijbWbqGo6UrjQpRVGFk0nTprFxdzmVq40qNOeNDk7gnhZrjNp/NHAq5xatYUhVMb989n9MH/adpu/VV7vK8WowK3jq+lMZHq2J2ryeA799mqo33gAg9ac/wTa461VkQi3joYdwfPIJid+8JtRdEUIIIUQYkMD9OJU5XNz1tlHJpMFjpGrcPT+fERmxOBt9HVZTWb67nNZ1T1oH4N7aOmq//ALHZ5/hOXAQtEZrzau1DRRW1rM7vj9r0kdw9S1zjnnyZ2va6+XQ438DYF9cP77OGM0fZg9tCtqzkyLbVGypb/QSZzd+lD7ZfBCAsVnx9E8Izs1EUaUTq1mxNWkAe+P6MajmIBfuWUFRxdVN5/35tlKU9vHjyrVk//oFKjdvptJ3JHUmctw4km64ISj96S0RWf1J/t5Noe6GEEIIIcKEBO7HaWNRdYvRb4BGj4+LH1+GzWoCDQ/MbTkCX1nXyPPL9wFgNimirGbc/pH6ZOWh+r33qPnkU+qWLm134aNIIBfIrSzion0r0Gv/S+Hk04iZNYu4Sy/Bkph4TOfSWFDA/nvm4czLA+CV3HPITIzmG6dlN7VJi7XzwJwxzFuQj1kp6hqNkfc/fbiVZ284jY83GYH7+UEabQfjZkGhQCk+GjiVWze+wxkFa8mMMN5ba83n28q4dttCzt++kECykbJaiZw4kejTTyfxumv7RIqMEEIIIURHJHA/Th9sPNDiudI+RpfvZVbxOgbWHKTeaqdsVRR7pgwjJiWRiP79efmwDVedj9hoO/N/NI2amjrStqxFPf9ndn7xRYtgXVmtRJ9xBvaxY1AmM5hMKJPC19DA6gWfkV2yA4vHTd3y5dQtX07Zo48Sd+EFJH7rW9jHjUMp1brLbWifj8rXXqPsz4+inUau/LuDp7MscywPnjUUm6VlwDt3YhbTh6VQVOFky/5qfvPuZr7ccYibX1jNwRojbA5WfjsYNwsPXTmGX765gc+zJnHT5g+J9DZi+3IRfPMaNu+vIWfHer69fSEAseeeS8I11xA1aSKmyN5dZVYIIYQQoqdI4H4cdpU5eG9DCSbtY4jjIGft38C0wjzS6ivbtG0ozKPB/+9zgFnKhCszB3vZe/hWraK2traprbLZiDlzBrHnnU/M7FmYY2Laff+I2VdxzX+WMrFsB79OrsT01Zf4HA6q332P6nffwzZqJInf/CZxF1yAOS6uzf5aaxq2b6fskUeoW27Ua3cnpfD7EXNYm5aLwvhEoD1psXbSYu1MGpDIjtJaXlpRwOfbDxn9BzYUVTEktf1+H4u5E7OoqGvgTx9uY1nWeM4pWE3lG6+TcM03WLFsI3esfQWAyPHj6f+XR1ERvbe6rBBCCCFEb1Bat11hUhyhlKqKi4qKL3j5v+DzoX1e8Ppwlx5k+eK1ROwvIqv2MFafp8V+OxKyyEvLxep1E+t2MjpOMczu4/DWnUTVVbd9I7OZ6GnTiL/kYmLOPgdzTPRR++bzac557Ev2HKrjqklZPHLxMKo//JDKV16lYevWI+cQEUHM2WcRf9llxEyfjvvgQWo+/JCaDz+kYeeupna2iy/hStNUKs1H8uXtVhNL7pzdaQ59cWU9Mx5ZTPMfpa7s1131jR6m3L+I/gd289iSvwMw4KUX+frnvybzcBGumHhO+eAdrP2CN9ovhBBCCNFbEhISqK6urtZaJ7T3uoy4d4F2uThwzz1ttue2em4dkEP8JZeyctAk7lpZhdVkoq7RQyAF/vLxmbybtZ9EVw0PjrFxqvsQjQUF2E8ZTdwFF2BJTu5Wv0wmxfemD+LXCzbx7voS7jw/l7SrrybhqqtwbdhA5auvUvPpQrTTieOjj3F89DGm6Gh8dS0XMIoYMIC0O37FuuxxVPnryjedUxcq3ZTWNBBlNTflu3d1v+6KirBwxYT+vORyU5LYn/6VJRT+8Edk1tXhVSZc9/xBgnYhhBBCnLAkcO8KpbBkZqCUCUwmvMCuRis7bSl4swdwy3WzsQ0ZjDUnB6UUlwCTZ7ooqnCSEW/nd+9tZuGWUt5dvx+AKnsctePGknJqdqdv2xVXTsziL5/uoKKukYc/3sZdF44gLdZO5PjxRI4fT/pvfotj4UKq332X+pUrm4J2lZJC4sUXE3fJxdhPOQWlFC+9uOaolW7ak50UibfVJzfBLlEZcO2UHF5aUcA72ZO5rXIB2n8+/x1zMfddfnbQ308IIYQQIlxI4N4FppgYhn3+OQDz84q56+2NuP012G+fPZTYs1qPvR/JAQf43aWjWLS1tGnkXQP3vruJM3NTj3tE2m41c+qARD7dUsrbeSV8sPEADzarYmOOiSZhzhUkzLmC9xbm8ckL71Iek8LGlMH86fxxzB1jtHt3fQkLt5QCYDErIi1HKt0crY/NK81YTaYu73csRmbEMTEngcWNE7ll6/+wuhtYljGG8ouvbjOJVgghhBDiRCKBezeUOVzcMz+/KWgHeGbZHr4zbcDRU0kiLNQ2HMmDD1YqSZnDxZc7DjU9b/AYK5lOH5bS4thlDhd3fFlKQ9ZkY4MH5i0w6s0XVTj59YJ8AM4ekcaDc8dQVOnssAZ9e5pXmunOfsfi2ikD+FVhFY+cei2jK/bx4tCzuW9Ueo+9nxBCCCFEOJDAvRu+3lVOo9fXYltXVzv1+FruF6xUkqIKJxFmU9PiTwCWdvpUVOHE1yqdpcHt47K/f4XW4NWa6AgzD105ltRYG2lx3Q+8m3/K0JMuGZvBH97fzLL00SxLHw3A7Ny0Hn9fIYQQQohQMoW6A32B16d5ZWUBd8/fSOsiPF0JwAOpJHariVibBbvVFLRUkuykSNytbgpcbm+bPrm9vhafFICRsuPx6ab89EavD90myz382K1mxmXHNz1XwLJdh0PXISGEEEKIXiAj7l1Q3+hl3oJNACREWqhv9GGzdC+Xu6dSSZrnl3t9GrdXE2ExERXR8tI+vWQPYAS50TYzTrcXn6ZlCUeLOeiVYHpCmcPFyr1HauVraDc9SAghhBDiRCKBexcEYluTgtd/cDqJ0RHHFID3VCpJ4KZgY3E1t/13LfWNXl5Yvo/bZg8F4Ovd5SzaVgbAQ1eOYWhaLJERJuY+uRyX+8hofU9Vggm2ogonNrOJxmbpQT1RflIIIYQQIpxIqkw3REdYqG3wNq0YGk5BYlqsnXNGpvPdaYMAeGrJHmpcbnw+zYMfGYsxTRqQyDdOzWbSgERGZcT3WPpOT2svPaiv3HQIIYQQQhwrGXHvhr4QHP7gzMG8vKKAaqebZ5ftZXBqDBuLjZVa5100AqVUU9verAQTTL1ZflIIIYQQIlyENHBXSmUAPwWmAKcCMcBsrfUX7bS9DLgPGAWUAf8B7tdae1q1SwAeAeYAUcBK4Bda6/XH3E/oMyPSyTE2bjxjIP9YvJunl+whMsKobX7B6H5MGpDUpn1vVYIJtr560yGEEEIIcaxCnSqTC9wFZAEbO2qklLoQeAeoAH7s//dvgcdatTMBHwLfBP4O3AmkA18opYYcayejIswsuXN206JG4e7mGYOxmRV1jV4O1zYCML5ZFZYTRTimLAkhhBBC9JRQp8qsBVK01uVKqSuABR20exRYB5yvtfYCKKVqgHuUUn/TWu/0t7sKmAbM0Vq/42/3BrAD+B3wnWPppNmk+lRw2Oj10aryI39dtJO5k7L61HkIIYQQQogjQjrirrV2aK3LO2ujlBqFkR7z70DQ7vckRv+vbLbtKmA/8G6z9zgEvAFcoZSyBqvv4ayowond2vLSBqquCCGEEEKIvinUqTJdMcH/dU3zjVrr/UBxs9cDbddq3XqZJFYBscDQ1gdXSlV19gD6XI6JsVJry29BX5hYK4QQQgghOtYXAvcM/9cD7bx2AMhs1bajdrRqe8LqyZVahRBCCCFEaIQ6x70rAsPEDe285sKoHNO8bUftmh+ridY6obM376uj7lJ1RQghhBDixNIXAvdAYratndfszV4PtO2oHa3anvD6aqlHIYQQQgjRVl9IlQmkuWS081oGxmTU5m07akertkIIIYQQQvQZfSFwX+//emrzjUqpTIz67+tbtZ2kmi8PapgC1AK7eqSHQgghhBBC9LCwD9y11puBbcAtSilzs5d+BPiAt5ttewtjAurlgQ1KqRTgauBdrbW753sshBBCCCFE8IU8x10pda//nyP9X69XSk0HqrTWT/i33QG8B3yilHodOAW4HaO2+45mh3sLWAG8qJR6FDgM3Ipxg3Jfj56IEEIIIYQQPUi1LXneyx1QqqMOFGitBzZrdwXG6qcjgUPAs8AftdaeVsdLBP4MXIFRRWYV8Eutdd4x9q8qPj4+vqqq6lh2F0IIIYQQoksSEhKorq6u7qjqYcgD93AngbsQQgghhOgNRwvcwz7HXQghhBBCCCGBuxBCCCGEEH2CBO5CCCGEEEL0ARK4CyGEEEII0QdI4C6EEEIIIUQfIFVljkIp5QNUfHx8qLsihBBCCCFOYNXV1QBaa93u4LoE7kfRrM58dUg70nNi/F9rQ9qLnhW465Jr2DfJ9evbTvTrB3IN+zq5fn3biXb94gCf1rrdRVJDvnJqHxC49UkIcT96hFLqCwCt9azQ9qTnKKWqQK5hXyXXr2870a8fyDXs6+T69W0n+vVrTXLchRBCCCGE6AMkcBdCCCGEEKIPkMBdCCGEEEKIPkACdyGEEEIIIfoAqSpzFCf6pI6TgVzDvk2uX98m16/vk2vYt8n1O7HIiLsQQgghhBB9gATuQgghhBBC9AGSKiOEEEIIIUQfICPuQgghhBBC9AESuAshhBBCCNEHSOAuhBBCCCFEHyCBuxBCCCGEEH3ACRu4K6UylFIPKaUWK6UcSimtlJrVTrt4pdQ/lFIHlFIupdQGpdS1XTj+//zH/Gs7r/VXSr2slDqslKpXSq1QSp0XlBM7iQT7Giql7vMfo/XjYDttf62UelcpddDf5r4eOckTWKiun1IqSSn1glJqq/99q5VSq5VS1yulVM+d8YklxL9/7bXTSqkf9szZnphC+Dt4QyfXUCulruu5sz5xhPh3UOKYMGUJdQd6UC5wF7AL2AhMa91AKWUBFgLjgCf8bc8H/quUsmitX2zvwEqpi4EzO3gtAVgGJAGPA6XAN4D/KaXO01p/fnyndVLpqWv4A6C+2XNnO23+hHHt1gEXHMc5nMxCdf3igMHAAqAQMAPnAC8Cw4DfHvspnVRC+fsH8AnwcqttK7tzAiJk13AJcH07+/3c/z6LuncaJ62QXD+JY8Kc1vqEfACxQLL/31cAGpjVqs01/u3fabX9LYwf1Ih2jhsB7MD4z18Df231+l3+7Wc222YCVgHrQ/196UuPYF9D4D5/24QuvPdA/9cE/z73hfr70dceobx+HfTnPaAGfxlceYTv9Wvvb6s8+tY1bKcvkf7fv09D/X3pK49QXT8kjgnrxwmbKqO1dmity4/S7AyMH843Wm1/DUgDZrezz08x/gA92skxD2itlzTri8//HuOUUrld6L6gR6+hUkrFdZY2obXe152+irZCef06UABEA9Zu7ndSCofrp5SKVErZu9Rh0UY4XMNmLsUIRP/bjX1OaiG8fhLHhLETNnDvIhvgARpbbQ98hDSx+UalVD/gN8A8rXU97bPR/ke/7R5THLduXUO/QqAaqFZKPauUSurB/onO9dj1U0rZlVIpSqmBSqnvADcCy7TWrd9LHLue/P37PlAHOJVSG5VSc4LRYdFGb/0NvQ7j/8b5x9pR0a6euH4Sx4SxEznHvSu2Y4y+TQZWNNs+w/81s1X7B/37tM67bH3Ms5VSWVrr4i4cUxyf7lzDSuDv/naNwFkYuX4TlVJTtNYNPd9d0UpPXr/v+9sHLAJuCFrPBfTc9VsOvA7sBbIxPumcr5S6Vmv9ak+cyEmsx/+G+gPDC4B3tNaO4Hb/pNcT10/imHAW6lyd3njQcW5YP6AK2IYxeW0gcAvGnagGnmnWdjLgBaY329ZejvtYjF+Ir4HTMSbJ3QO4/O3vDfX3oy8+gnENOzjurf52N3fwegKS494nrx+Q5T/mN4GXgM+A4aH+XvTFR6h+/5q1i8YI4ouQOQp97hr6j6eBy0L9feirj968fkgcE9aPkzpVRmt9ELgMI2d9IcZ/DH8GfuxvUgtGMhjGzOq3tdbLjnLMjcC1GNUrlgO7gZ8AP2t+TBEcXb2GnfgXxsd/Z/dUH0XHevL6aa2Ltdafaa1f01pfjzGp/DOlVGSw+n+y663fP611nb9tFkalDREkvXQNrwMqgI+Oq7OijZ64fhLHhLeTPVUGrfUSpdRgYAzGqM4GjnwMtNP/dQ7GiPs8pdTAVoeI828r1Vo7/cd8Syn1HkZ5JjOQB8xqdUwRJF28hh3t61NKlWCUvRIh0IvX7y3gRxilXD859h6L5nrx+hX5v8rvapD15DVUSuVgpFg8pbV2B6/XIqAnrp/EMeHrpA/cAbTWXmB94LlS6hz/PwO1SnMwJvK2V7v0Rv/jQuDjZsdsBFa3OmYD8FUQuy78unAN26WUsmLk0K7urJ3oWb10/QIj7fHH0EXRiV66foP9Xw8dQxfFUfTgNfwWoJBqMj2qJ66fxDHhSQL3VpRSqRg1TD/RWm/1b34f2NdO8wXAB8B/MO5GOzrmMOCHwPNa66pg9le01cE1RCmVqrVu/Z/+HYAdGYENG8d7/TpoB/A9jPzMDn9XxfELwvVL0VofbnXMZIxc3L1aaxnt62FB/ht6LUYVk07TTEXw9MT/gRLHhI8TOnBXSt3r/+dI/9frlVLTgSqt9RP+Nssw/qDswpjk8QOM0fUfBI6jtd6NkePV+vgAu7XW7zTbZsH4mOotjD9WgzF+2AuBu4N3dieHYF1DvwKl1GvAJoxRg9nAlf59X2n1vtcDAzD+oAGc2awvf9daVwfnDE9sIbp+tymlrgA+xLjhTgTmAlOAJ7XWu4J4iie0EF2/25VSl2MMihQC/TEm26VhTNAT3RCqv6H+456CMdHxIa2NWY+ie0Jx/SSOCXOhnh3bkw+M0bX2HvuatXkc2IPxQ3wQY/Q8sxvH/2urbSaMMmZF/mMWAv8HxIf6+9EXH8G8hsDTwBbA4W+7HfgDENlO2y86ee+Bof6+9JVHKK4fRj7tgma/gw6MCVY3IRVJ+sL1Ow9jkt1BjMoWFRg3YWeE+vvRFx+h+hvqb/+g/73GhPr70FcfIfodlDgmjB/Kf5GEEEIIIYQQYeykLgcphBBCCCFEXyGBuxBCCCGEEH2ABO5CCCGEEEL0ARK4CyGEEEII0QdI4C6EEEIIIUQfIIG7EEIIIYQQfYAE7kIIIYQQQvQBErgLIYToFqXU80opWQRECCF6mSXUHRBCCBFa3QzCB/VYR4QQQnRKVk4VQoiTnFLq2602zQBuAZ4ClrZ6bQHQCJi11q5e6J4QQgg/GXEXQoiTnNb65ebPlVIWjMD969avNePu8Y4JIYRoQXLchRBCdEt7Oe6BbUqpZP+/DyulHEqpd5RS/fxtblFKbVVKuZRS25RSl3dw/GuUUsv8+9crpVYqpa7qjXMTQohwJoG7EEKIYPoYiAd+CzwNXAIsUErdAdwBvADcDUQAbymlWuTMK6X+BLwGOIDf+NvWA28qpW7rrZMQQohwJKkyQgghgmmV1ropwFZKAfwc6A+corWu8W//HNiAkZJzj3/bRODXwINa63nNjvk3pdQ7wINKqRe11o7eOBEhhAg3MuIuhBAimP7a6nlgcuuLgaAdQGu9EagBhjVrex2ggReUUinNH8B7QCxweo/1XAghwpyMuAshhAimPa2eV/q/7m2nbSWQ3Oz5SEAB2zo5fvqxd00IIfo2CdyFEEIEjdba28FLHW1Xrf6tgQs7ab/5GLsmhBB9ngTuQgghwsVO4AKgUGu9NdSdEUKIcCM57kIIIcLFS/6vDyilzK1fVEpJmowQ4qQmI+5CCCHCgtZ6tVLqPuA+YL1S6k1gP5ABTAIuwigjKYQQJyUJ3IUQQoQNrfXvlVJrgJ8APwOigTJgk3+bEEKctJTW+uithBBCCCGEECElOe5CCCGEEEL0ARK4CyGEEEII0QdI4C6EEEIIIUQfIIG7EEIIIYQQfYAE7kIIIYQQQvQBErgLIYQQQgjRB0jgLoQQQgghRB8ggbsQQgghhBB9gATuQgghhBBC9AESuAshhBBCCNEH/D91nIiQETZKRwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- PLOTTING --- #\n",
    "# Plot the forecast.\n",
    "fig, ax = plt.subplots(figsize=[12, 7])\n",
    "\n",
    "# Plot training set.\n",
    "y_train.plot(ax=ax, marker=\".\")\n",
    "# Plot actuals in forecasting horizon.\n",
    "y_test.plot(ax=ax, marker=\".\", alpha=0.6)\n",
    "# Plot forecast.\n",
    "y_forecast.plot(ax=ax)\n",
    "# Plot 1 step forecasts in training data.\n",
    "y_forecast_train.plot(ax=ax)\n",
    "\n",
    "ax.legend([\"train\", \"test\", \"forecast\", \"in-sample forecast\"])\n",
    "ax.set_xlabel(\"Time\")\n",
    "ax.set_ylabel(f\"{target}\")\n",
    "ax.set_title(f\"Recursive forecast with {model}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b329343e-5055-4a8a-903d-4ec0252239a4",
   "metadata": {},
   "source": [
    "Let's compute the RMSE of this forecast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ca4d4411-a247-47cd-882c-bd67833b69f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46.87136907684174"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute error metrics.\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "mean_squared_error(\n",
    "    y_true=y_test.loc[y_forecast.index], y_pred=y_forecast, squared=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "524094c3-1093-4fa3-bddc-d7672ee41a3f",
   "metadata": {
    "tags": []
   },
   "source": [
    "Feel free to change the dates, try different models, and different features!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d649f5f-cfbb-43b8-83d1-b168514acb9b",
   "metadata": {},
   "source": [
    "In this notebook we have shown that seasonal lags can help capture seasonality. In lectures we showed that seasonal lags do not always capture the seasonality well, for example, try using just seasonal lags with the Electricity Demand in Victoria dataset as we showed in lectures. Seasonal lags don't explicitly capture how seasonal aspects of the calendar (e.g., the months of the year) introduce seasonal effects. So let's look at some other ways to capture seasonality in the following lectures and notebooks!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
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   "interpreter": {
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